He graduated from the University of Qinghua undergraduate in physics and is studying as a doctor of engineering physics.
First, of course not. However, it is true that the following two points are to be considered and psychologically prepared.
First, be prepared to face severe school stress.
The polarization within the science and technology profession is systematic and there is a growing trend. Although the State needs scientific and technical personnel, this is a high-level qualification for core competencies and has nothing to do with most graduates. The path to core competencies is rugged and will require a great deal of effort with a certain gift.
Second, to prepare for the end-of-the-life only front-line.
For the vast majority of those who ultimately lack core competencies, entering the industry line is the end. The first line of work in industry will require technology rather than knowledge, and by that time, education will not be of much use, whether you are 985, 211, or double-departure, second-level, specialty graduates.
Next, I will systematically introduce the four lines of attention and employment logic in choosing science and technology, which will help you to plan your studies and employment ahead of schedule, without turning the curve, to do more than double the job:
1 The selection of four subjects of care for the scientific and technical professions: the need for a two-tiered industry, the fact that most people are not going back, the specialization in the science and employment trade, and the expectation of a correct mindset;
2 Science and Technology Employment Logic: topics such as professional interface, transfer, cross-cutting disciplines, future options, and the Skyhole profession
The selection of four science and technology professionals
1 Polarized industry demand
Both in terms of professional learning and in terms of industry needs, SETs are bound to be polarized, and the current model of industrial development is destined for this.
A few of the best people occupy almost all of the good opportunities and do not lack jobs, nor are they restricted by one or two trades; but the vast majority of the remaining people have to go to essential skilled workers.
This corresponds to a systematic dichotomy of educational and employment patterns.
At the learning level, some of the best students are able to master the whole knowledge system and move around on that basis; most of them have spent four painful years in order to acquire these difficult knowledge, and eventually find only what they have learned useless.
In terms of employment, the jobs in the industry have gradually been divided into a small number of high-end jobs close to scientific research and low-end jobs, most of which require book-based equipment, with fewer buffer zones.
As technology progresses, there are fewer and fewer people-oriented links in actual industrial production, and a great deal of knowledge is encapsulated in industrial software and equipment. Engineers who operate software and equipment do not need to understand them. They simply need to learn the basics of general use, and then to do something in accordance with the instructions.
This pattern has profoundly changed industrial production: 100 technical engineers were required to provide technical guidance in 100 first-line production units; but only 10 basic technology engineers were required to develop a highly automated set of equipment to encapsulate specific technologies, and only a few skilled workers who could operate with instructions could be found in 100 first-line production units.
The need for polarized industries also dictates that the way forward for the industrial profession is necessarily polarized. At the extreme, modern industrial development requires only two categories of college students (first-line technicians) and Dr. Suk (research and development staff). If Dr. Suk’s demands are not met, he will have to do some college work and get paid.
The need for research and development staff in each industry is limited, and this is the most direct reason for many professions to become a “pit.”
Many industrial clusters are large in size, such as materials, chemicals, civil wood, water, electricity grids, nuclear power, etc., but their demand for research and development personnel is limited. As a result, many “high-quality students” have graduated without being able to find a job suitable for themselves, and have found themselves in a position where their colleagues are significantly less well educated than they are, which leads to an exasperation that the profession is a pit.
This pattern of industrial development is not altered by the emergence of frontier and cross-cutting areas. Even with the emergence of more “smart cities”, “smart electricity grids”, “supervised production through new technologies” and high-profile industry demands, it has created more jobs requiring post-secondary students than Dr. Suk.
The fact that many higher education institutions are now continuing to develop “AI X Engineering” does not help – higher-level talent is less developed, and graduates end up as skilled workers.
Two, most of them didn’t turn back.
Many prefer to compare the overall situation with the science and technology specializations of schools like Chinghua. If we set our target on Qinghua, we will find that the exit route for graduates in the science and technology sector is actually not bad, and that, while there are times when we cannot find a good job in the industry, there is no difference in treatment as long as we work hard.
In other words, those who have learned the science and technology profession have to turn back.
But it’s Qinghua, and it’s not so hard for people who can get into Qinghua, but 99% of them, in absolute difficulty, simply don’t meet the transition threshold.
“Mixed Talents” transferred from science and technology are generally favoured in all walks of life. For example, the transfer of a doctorate in mathematics and physics to finance, the transfer of technical graduates to auto-driving, computer, chip manufacturing, or to consulting, legal professions, etc.
For students at the front school, when the development of the industry directly corresponding to the profession is hampered and it is difficult to find a satisfactory job, the move seems to be a minor matter.
But all transfers need to be made on a solid professional basis, people who move to finance need a good mathematical base, people who move to computers need a good code capability, people who move to auto-driving or chip making need a good soft, hardware capability, and people who go to counseling, law need to have a deep understanding of their industry.
For example, the Physician doctor willing to work as a detector on a 40+ annual salary is not seen as a detector’s trade, but as a doctor who can design, debug and conduct research on his own initiative is naturally capable of helping him design new similar hardware.
Specific, narrow-appliance technologies are never popular, and those who fail to cross the threshold and exercise core competencies end up with narrow-appliance technologies.
In the final analysis, complex talent is popular either because they have a stronger base than graduates who remain in the same profession or because they have a deep understanding of the logic of the whole industry.
Both are quite difficult to access and are not automatically available upon graduation from a seemingly high-quality technical and scientific profession.
Acquiring a good mathematical base requires you to chew down the math cores of math analysis, mathematical equations, four-strengths, philosophies, troupes, and so forth, not to deal with the examinations, but to reach a relatively systematic understanding. These theoretical bases are then used to model, solve and provide viable solutions to problems actually encountered.
The ability to acquire a good code requires you to learn the theoretical basis of data structures, compilation principles, computer networks, databases, etc., while working on specific projects, going through countless days and nights of debugs, and fully understanding the code you write.
Acquiring an excellent electronic (hardware) capability requires that, based on an understanding of the signals and systems, modulations, electrons, microwaves, etc., you design or debug and maintain a set of equipment or systems, so that you can deal in the field with the inexplicable malfunctions encountered in practice until it can function properly.
Any of these three competency requirements is higher than the requirement of the majority of institutions for graduate masters (even doctorates).
It’s no big deal to graduate without a lot of core skills, with cell breeding, killing mice, burning stoves, overposting, using matlab to do a limited amount… These are extremely repetitive, and there are not a few Dr. Suk who have been able to save up experimental data on the work they can do directly with a high school student.
Most of the subjects allocated to students at non-predominant institutions are, in fact, of little technical content and require their own core competencies, and not a lot of subjects to improve, adapt or even build a mathematical model or a soft, hardware system from scratch.
The number of students enrolled in master’s degree is now over one million, and the graduate students are no longer `employer education’, learning how to use a package of sealed software, models or equipment, and then changing parameters to compare them, which is the path to graduation for most students.
Without these excellent core competencies, the transition would be a fresh start. The original science and technology professional education will not bring you any help — you’ve been in college for four years.
To be honest, it is no shame not to learn about core competencies, and we must admit that those who have the talent to master them and to use them further to deal with difficult subjects are the minority.
If it wasn’t for the first 1% of the day, then all you had to do was put yourself in place, and don’t fantasize that you had a retreat like the first 1%.
Specialization between scientific research and employment
In fact, purely basic disciplines are rare and exist only in top-level institutions. The scientific and technical specializations of most institutions are intermediaries between basic subjects (mathematics, physics, computers, electronics, etc.) and real engineering, and serve as links between basic disciplines and practical engineering.
In the case of the undergraduate development programme of the Qinghua Engineering Physics Department (i.e. nuclear specialties) where the author is located, the basic course includes both the mathematical equation as the basic course of the physics department, the Quantico, and the basic course in electronics such as modulus, signals and systems, as well as certain computer-based basic courses, while the specialized course is based on nuclear engineering principles, radiation protection, etc., and is conducted directly in the context of practical nuclear technology applications.
This positioning has divided the knowledge systems of most SETs into two parts that are less relevant: the foundation-oriented and the engineering-oriented.
Theoretically, in a complete set of scientific and technical knowledge systems, the interface between the two components is achieved through the “application of basic knowledge in the context of specific engineering experiences”.
However, this interface is often complex: engineering conclusions are difficult to simply draw from the knowledge of the basic curriculum, and small conclusions in the specialized curriculum are often quite complex in context and interpretation.
It is no less simple to understand such a science-to-work interface than to dig deeper into basic disciplines. The basic curriculum is difficult to master and combines the complexity of this interface, making it necessary for technical students to bear the greatest stress of all professions.
If students and schools are under pressure to be “qualified” in the knowledge system, then students who eventually meet the requirements for graduation will not constitute an absolute majority even in Qinghua, and a large number of polytechnic faculties have a normal graduation rate of less than 90 per cent, and the hanging rate for some of the hard core courses is even less than a quarter.
And this is Qinghua…
Learning difficulties exist objectively, and it is largely impossible for the vast majority of ordinary people to understand the whole body of knowledge. This has led most schools to further separate the two parts of the knowledge system, to castration of the number and difficulty of basic courses, and to a more focused approach to the examination of professional subjects, which is simply not “understood” at all.
Objectively, the scientific and technical professions have developed training programmes that exceed students ‘ affordability, mainly because of the prevailing demand for scientific research and even the pressure on employment.
This is also influenced by the industrial development model:
In the original case of 100 engineers directing 100 production units, each engineer needs only to consider his or her own plant, and six points is enough;
But with the polarization of the industrial model, today the 10 engineers responsible for the development of the equipment have to think as thoroughly and as carefully as possible, so that 9 points are passed;
As for the remaining skilled workers, there’s a three-point level.
The effect of this is that the country lacks 9-point (high-end) polytechnics, so that those with core competencies can do well in all walks of life after graduation; but most of those who graduate from polytechnics can do only 7-points, and they can’t reach the current stage, waiting for only skilled workers.
Against this background, institutions naturally aim at developing nine-point talent by adding to their training programmes and creating a paper-based system for developing high-level talent. It is difficult to castrate when the actual landing takes place in order to graduate most students.
Four, set your mind straight.
However, irrespective of the systemic problems that exist in the science and technology profession, they remain the main choice for most of the candidates.
So I would not suggest that the seniors “do not choose the science and technology profession” but rather give the “cautionary choice of the science and technology profession” that the following two points need to be considered and psychologically prepared before making a choice.
First, be prepared to face severe school stress.
The polarization within the science and technology profession is systematic and there is a growing trend. Although the State needs scientific and technical personnel, this is a high-level qualification for core competencies and has nothing to do with most graduates. The path to core competencies is rugged and will require a great deal of effort with a certain gift.
Second, to prepare for the end-of-the-life only front-line.
For the vast majority of those who ultimately lack core competencies, entering the industry line is the end. The first line of work in industry will require technology rather than knowledge, and by that time, education will not be of much use, whether you are 985, 211, or double-departure, second-level, specialty graduates.
But entering the production line does not necessarily mean failure.
For example, on the same site, there may be both non-civil and homogenous graduates; this is no doubt unacceptable to fellow graduates, but in turn, it has led to a higher value for money in the engineering profession in ordinary schools: And in a way, it’s like being on a line with 100 points higher than Cocao himself.
Professional employment logic in science and technology
1 Introduction to the theory of “competence”
This is more of an expression of the profession than of the time when the economy was planned in the early years, and the work packages for university students were distributed.
The situation at the time was one of “planned economy”, in which the State planned to train its personnel according to need. As a result, in the 1950s, a large number of polytechnic schools were established, such as the Beijing Air Academy, the Beijing Iron and Steel Institute, the Beijing Geology Institute, the Beijing Petroleum Institute, the Beijing Teacher Training University and so on.
At this point, the professions and professions, as well as the professions and jobs, are clearly identified, and naturally the terms “professional counterparts” and “opportunity posts” are used.
This distribution mechanism is accompanied by a training model that directly targets specific industries, jobs and, in some cases, professions that directly target specific industries, such as the Department of Industrial Economy and the Department of Agricultural Economy, which were established by the Humanities Congress and directly by the Ministries of Industry and Agriculture.
Under this model, students receive a range of skills training at the undergraduate level for specific professions and jobs, with a greater focus on professional skills, and theoretical courses exist as part of such skills training, more like a professional “engineer” model.
As a side effect of this model, a range of problems, such as the limited knowledge of graduates, the poor system and the relatively weak scientific capacity, were also prevalent at the time.
Of course, the profession itself is directed at certain industries, and there are more disciplines within universities, but the concept of professional counterparts or counterparts has been reinforced in our planning economy.
However, the distribution of work packages and the planned economic era of targeted jobs have been reversed.
On the one hand, with social development, higher education places greater emphasis on educational patterns, leading to a major merger in the 1990s, with almost all single-scientific schools disappearing and moving towards integration.
More emphasis is placed on literacy education in specific skills development, and on strong foundations rather than skills.
The institutional set-up, which was designed to target industries and even jobs, has been declining and has been replaced by a training model for scientific research (thesis), which has also become the main source of many of the following “separations” and a range of employment problems.
On the other hand, as the market economy has grown to this point, our industrial system has gradually risen to a higher level for decades, and the sectors and jobs have become more and more sophisticated, far from the “package distribution” era.
The interaction between higher education and the market has made the matching of professions and professions, jobs, more flexible and vague, much more complex than a simple description of the term “competent professional/post”. This is particularly true for undergraduate staff. The development of this model to date has given rise to two facts that were difficult to imagine for the previous generation.
First, undergraduate training programmes for employment and support for employment have become a minority, and this is more evident in higher (scientific) educational institutions.
The most extreme examples, such as Tsingbei, are applied-oriented professions, such as law, finance, management and software engineering, which by definition correspond directly to certain trades/posts, most of which focus on teaching their own knowledge systems at the undergraduate level and do not develop employment-oriented skills. If better employment results are to be obtained after graduation from the undergraduate studies, it will often require considerable planning capacity or long remedial courses.
Second, because of the wide range of cross-disciplinary and cross-directional approaches, training programmes oriented towards research rather than employment have reduced barriers between professions – – If, of course, you can master a knowledge system, which most people cannot do for their lifetime, so that the benefits of reduced professional barriers only apply to head graduates.
These two underlying facts will be the subject of some detailed analysis.
2 ubiquitous “go-round”
2.1 General information on the “transmission”
The general impression of “transfer” usually comes from the topics of transition to CS, to finance, to law, to teacher training, to examine students and so on.
If, on the other hand, this general impression is followed by a sharp dichotomy between “opportunity” and “reversion”, it is easy to assume that the employment orientation of each profession, in addition to that of a direct counterpart in the profession, is to move to these popular occupations.
However, there is a significant deviation between this understanding and the actual situation.
The real employment situation is closer to a situation of “go-and-go” than the simple dichotomy of this kind of cross-border and trans-shipment, which is largely reflected in the non-situational technical specializations in which the training system is primarily geared to scientific research.
In our articles on various professions, this pattern of employment is often referred to as “advanced into the world of practice” — typical examples are the Dr. Physics who went to China to make hardware, Dr. Mathematics or Physics who went directly to hedge funds after graduation, nuclear engineering that ended up in space companies, electric engineering that worked hard to clean the ground after graduation, electronic information that ended up in drug research and development, etc.
Examples of this type are quite rich in professions other than the pits.
Technically, there are significant differences between the faculties of graduate students and the jobs that they last perform, but, if analysed carefully, there are some links between knowledge and competence systems that follow every example of ” running around ” .
Dr. Physics can do hardware because many Doctors in physics themselves need to design instruments, and thus build up hardware-related knowledge, with the help of a doctorate, and it is natural to enter China (reference physics);
Dr. Nuclear Engineering can go to space enterprises because there are several directions in nuclear engineering that require knowledge of plasma physics, which coincides with space engineering needs (reference to nuclear engineering, space presentation).
Dr. Electric Engineering can be a ground sweeper because the knowledge of electronics itself, regardless of the power and power of power, is highly condensed and equally meets the requirements of these electrical enterprises (reference is made to electrical engineering).
I don’t know.
The same “shifting” is very common at graduate level, and the pattern is largely consistent and is not discussed in detail here.
We can note that these efforts are not fully compatible with the original profession, but rather that some differences and some key similarities prevail.
However, on the basis of several similarities, these differences are not difficult to cross for well-established people, making it possible to “scramble around” employment.
Indeed, this is the benefit of literacy education, which is currently emphasized: it is based on solid foundations and has strong learning skills, so that it can move quickly.
2.2 Basic competencies and cross-cutting disciplines
As mentioned above, the core of the shift lies in some of the “key similarities” that seem to be prevalent across different directions.
Based on the personal experience of the author and the employment logic described in this series of articles, these key similarities, which are at the heart of the `reversion’, can be briefly summarized in three categories:
Numerical basis: In fact, it refers to the ability to push the formula, to solve the equation.
If the mathematical basis is good enough, then then it will be possible to move in the direction where theoretical work is required after a simple preparation.
In terms of employment alone, one of the main options is to quantify finance, and in fact hedge funds have always favoured well-known mathematics or physics doctors; and if undergraduate studies were to be considered in the direction of continuing studies, the potential alternative would be any science or technology (which, of course, requires a really good mathematical basis).
Code capabilities: You see code capabilities, you think about CS, but people with some kind of code capability are likely to go to quite a lot, not just Internet companies.
Essentially all companies have separate software development departments or units that combine software hardware, and a solid code capability is sufficient to be a knock-on brick leading to these positions.
Electronics: Similar to coding capacity, only hardware. People with a good foundation of electronics can eventually go to a variety of places to make hardware, not just Chinese (though China may be paying the most).
Most of the science and technology professions focus on the development of one to two of these three important basic competencies, while some of the most stressful subjects, such as electronic information (reference to electronic information presentation), nuclear engineering (Qinghua qualification), etc., are subject to a significant number of courses in these three areas.
If students are able to cope with the stress of their studies and learn about these three subjects, the way forward is expected to be considerably expanded.
I don’t know.
Except for the “transfer” that relies on three basic capabilities: mathematical, code, electronic. In addition to this, there is a wide range of other “shifting” models, i.e., “shifting” through cross-disciplinary or cross-directional approaches.
For example, the instrumentic professions were originally cross-cutting subjects such as electronics, opticals, mechanics and computers, which are covered in the curriculum of their development programmes. So it is quite normal for an instrument graduate to choose whether to dig deep into electronics and eventually to do hardware, or to study computer technology and finally to do software (reference instrument type introduction).
As a subdivision of cross-cutting disciplines, there is also a wider range of employment exports, such as bioinformatics, computational material, etc., and the range of career choices for graduates is generally much better than that of biology and material (non-crossing direction) as a “situational pits profession ” .
It is therefore a relatively common path to “defeating the pit” by entering a cross-direction (refer to life science presentations and bio-climatics to discourage articles).
But the cross-direction of this type of pit and non-pit specialization, though better than that of the pit, is no better than that of the first to not enter the pit (all thinking of students’ informatics, why not study computers), and I hope that this does not mean that the pit is not a pit.
3 Broader way out, higher threshold
The job prospects described above are initially quite optimistic, but they are not without cost:
A broader approach is based on solid knowledge systems, and it is much more difficult to master a solid knowledge system than to master a skill or simply graduate.
Graduation from the physics department does not require a solid mathematical basis, but simply a general backsliding of the most basic model. Even getting a Ph.D. in physics has nothing to do with having a solid mathematical base, but a large set of doctorates that adjust the parameters for simulation.
Graduation from the electron or industrial system does not mean that all three basic capabilities are really strong enough, even if they can do so in Qinghua; it is good to know both. If the perspective is kept low, many colleges may require low-level masters until graduation, i.e., drawing boards and limited meta-grids.
Instruments, automation, and cross-cutting disciplines appear to be “gold oils”, but most people end up learning to be unsophisticated, not only without the broad range of options described above, but also with the risk of finding no good job in any direction.
I don’t know.
Ultimately, those knowledge systems that are at the core of the right to choose, are based on knowledge systems or cross-cutting knowledge systems that require a certain level of basic intellectual and long-term energy to be mastered, and are not automatically acquired after graduation from any of the science and technology professions that are not pits of the sky: it is more difficult to master these knowledge systems than to graduate.
Thus, from the point of view of data and online opinion, the assessment of the employment prospects of various scientific and technical professions is often highly polarized.
It is also important to note here that the set of professional articles we have provided has limitations here:
For example, in the article on the nuclear engineering profession, it is mentioned that it is easy to move, but not everyone can learn to do so. Rather, this proportion tends to be quite small in general schools outside the top-renowned schools.
And if only the low requirements of mixed graduation are targeted and the knowledge system of the profession is not in fact clear, then what can ultimately be acquired after graduation is just one or several skills.
These skills are not difficult to master, and they do not have the gold to match the masters and doctorates in public opinion. As many people described, “a high school student can do it.”
The formation of the crater
In contrast to the types of professions described above, the so-called “sunholes” are those in which their knowledge systems hardly coincide with other professions or where their training programmes are directly oriented towards employment in specific industries. This makes the exit route for graduates very narrow compared to the various professions mentioned earlier, and when the industry is undersized, poorly treated, or the number of graduates is larger than the demand, science becomes a shambles of employment.
Biochemicals (references to biochemicals) are included in this category, civil engineering for employment (e.g., appliance, reference to civil engineering professional presentations), and finance (reference accounting, business management professional presentations).
In terms of employment logic, there’s a few cores behind the “Turnhole” profession:
Lack of development of core competencies based on the three fundamentals of logarithmic, coding and electronics, and of a general nature;
It is not a broad cross-cutting discipline; or, while it is in the name of a cross-cutting discipline, in practice there is still no cross-cutting capacity-building. If you call yourself “qx” or “smart xx”, you don’t even teach basic computer courses (separate mathematics, data structure, etc.);
Compared to the first two broad knowledge systems, the main goal is to develop one or more skills that are not difficult to learn.
It would be useful to start with a variety of professional training programmes and take the seats as described above.
The three points above are in fact a description of the training programme and, from a personal perspective, an additional one needs to be added: In many cases, what actually occurs is not the situation where the above-mentioned training programmes themselves have pits, but where students with insufficient or no skills fall into pits themselves.
Ultimately, what supports graduates in moving around is a universal, high-level knowledge system, which is not directly available after graduation from a profession that is not a pit, but requires considerable effort to understand and learn about the core of content before gaining that advantage.
If this is not done and only the work skills required for graduation are learned, then of course it will not be possible to achieve the broad transitions described above. And to be honest, most skills are not hard to learn, and naturally there is no core competitiveness.
At these times, whatever the students themselves learn, they become a “cinderhole” from which they cannot escape, and what they can do (in addition to overworking the knowledge system) is to pray that the business environment is not so bad.
5 Concluding remarks
The models described above, while difficult to understand for high school students, cannot be said to be unnecessary. A brief summary, more than 4,000 words, is used to distinguish between two categories of study and two categories of students, thus providing a simplest description of the employment pattern.
There are two types of specialization, namely, learning to improve and learning to improve.
Two categories of students are relatively capable of learning, with the ability to master basic or cross-cutting knowledge systems, who will understand the profession; and those who do not have such skills can only learn the corresponding skills and meet the requirements of graduation.
The classification of the profession is given in the previous section, while the classification of students is based on a high degree of individual diversity, which makes it difficult for the author to give a uniform description here and to give recommendations from a personal perspective:
If it is hard to learn from a higher examination, do not have too much confidence in their ability to master basic or cross-cutting knowledge systems. It is best to step down to a highly skilled profession and stop looking for a way.
Author: Chen Yu, undergraduate in physics, University of Tsinghua, with a Ph.D. in Engineering at registration number: YXA16RNQ1c53QzkljsJ1Jn
I don’t know.
Keep your eyes on the road.
