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Thread: Career questions

  1. #1 Career questions 
    Lee
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    http://archive.sciencewatch.com/jan-...eb99_page2.htm

    http://www.timeshighereducation.co.u...orycode=415643

    http://archive.sciencewatch.com/jan-...eb99_page1.htm

    Look at the content of these 3 links. Especially the middle link is disturbing. In molecular biology, biochem and the like one cites a lot. Does this mean excessive time is spent just reading other people's research? I'm an aspiring scientist, and I want to spend as much time possible solving my own research, and not just making preparations for solving my own by reading other's works. I find that way the best to become a successful scientist. I also would like to avoid to the largest degree possible, teaching and paperwork and other mundane stuff. Anyone know how I can go about to avoid such things?


    Mathematics and comp sci would be the best for me, if I'm interpreting the meaning of the data correctly. Also, doesn't comp sci, mathematics and theoretical phys have way more interesting and intensive stuff than chem, bio sciences and geo sciences? The math in the three later fields seem easy, just basic undergrad level petty much. Also, is econ worth considering? Again it seems like the math and challenge is lacking. Can a field even be called challenging if the math's simple? People say econ requires some other skill like psychology, but is this actually hard?


    And then there's philosophy. Seems like a useless, dead field to me. The only still thriving is philosophy of language. Linguistics is different as it doesn't depend as much as on math as other fields, but still has its own rigourous system. But can a field really be impressive if like Noam C. are highly regarded in it? Even with the increasing rigour in linguistics, isn't it too young and simple compared to fields like phys and mathematics?


    Nother thing I would like to know more about is job prospects. I hear dubious sayings about physics full, none becoming research mathematicians, CS is full of unexplored possibilities - any shred truth to such claims? How the job prospects in those other fields, econ, linguistics, geo science, bio sciences. Any type of info you can give on this, whether its statistics, degree of co-op required with other scientists, chance of ending up mediocre or horror stories - fill me in.

    The field I got the worst impression of is medical science. Seems like competition is though with its standout plagiarism and it's almost law-of-the-jungle (In a deceptive way of course). Thanks in advance.


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  3. #2  
    Universal Mind John Galt's Avatar
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    Lee,
    welcome to the forum. I would argue that one of the most important skills, or mindsets of a scientist is objectivity. This was glaringly absent from your post. Although you are supposedly asking for advice and input, you appear to have very clear judgements already in place. However, I'll pretend you do have an open mind and try to answer accordingly.

    In molecular biology, biochem and the like one cites a lot. Does this mean excessive time is spent just reading other people's research?
    Do you think you can introduce groundbreaking science without knowing where the ground is currently located? What is the best way of finding where that is? What makes you think authors have necessarily read every paper they cite? Why are you making a connection between the number of times a paper is cited and the number of papers one would appropriately read as background to ones own work?
    I find that way the best to become a successful scientist.
    And precisely how do you know this to be the best way?

    I also would like to avoid to the largest degree possible, teaching and paperwork and other mundane stuff.
    Perhaps you would be better suited to becoming a drug pusher, college drop out, or rock star. :wink:

    Also, doesn't comp sci, mathematics and theoretical phys have way more interesting and intensive stuff than chem, bio sciences and geo sciences?
    Mathematicians and theoretical physicists might well agree with you. Geneticists, embryologists, microbiologists, geochemists and planetologists would think you were crazy.

    Can a field even be called challenging if the math's simple?
    Yes.

    And then there's philosophy. Seems like a useless, dead field to me
    Dr. Rocket, one of the forum's mathematicians, would share your view. Others wouldn't.

    But can a field really be impressive if like Noam C. are highly regarded in it?
    At least he writes proper English. Probably best you stay clear of linguistics.

    How the job prospects in those other fields
    I would argue that if you are good at what you do then job prospects are always good.


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  4. #3  
    Lee
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    Do you think you can introduce groundbreaking science without knowing where the ground is currently located? What is the best way of finding where that is? What makes you think authors have necessarily read every paper they cite? Why are you making a connection between the number of times a paper is cited and the number of papers one would appropriately read as background to ones own work?
    If I understand things correctly, life sciences are generally more holistic, that is more interdependent than the likes of CS and math (Which are more reductionistic). Its hard to take one single thing and study it alone. Thus I surmised that one has to read other a lot of other people's stuff in life sciences, but not as much in CS and math.

    And precisely how do you know this to be the best way?
    I would like to maximise time spent research on the research subject in it self, and minimize time spent preparing to research. In it self, there's nothing productive about reading other people's stuff. It's only a means to an end, or maybe it even should be called a means to a means to an end.

    Mathematicians and theoretical physicists might well agree with you. Geneticists, embryologists, microbiologists, geochemists and planetologists would think you were crazy.
    The level of math is of course higher in math than anywhere else, and the uppermost part of theoretical physics has some pretty hard math too. Don't see that in chemistry. But of course, I might be wrong, but I've rarely seen people point out actual examples and concepts which require a different type of skill (And what this skill entails, most of all) other than math. some say physics require more visualization, but big a part is this of most physicists research? How much is it used in one subfield compared compared to another? Is "visualization" useless in for example particle physics while greatly useful and hard to master in astrophysics? Etc. What about, say geology? Does that require visualization, or at least have some other skill to make up for the lack of mathematical challenge?

    Dr. Rocket, one of the forum's mathematicians, would share your view.
    Sounds like an interesting individual. I'll try to find some of his posts which shows off his worldview.

    I would argue that if you are good at what you do then job prospects are always good.
    On the other hand, there are many hardworking science graduates and too few postdoc research positions. And I got the impression that medical is the most cutthroat if 'em all.
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    Lee
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    Welcome Lee !
    I have some remarks on your posts. I jumped when I read it.

    First, so called computer sciences are not sciences but technology. Which is different from Information Theory and other branches of mathematics. So, you might need first to understand the difference between science and technology. And considering linguistics and philosophy in general as useless and dead is a bit bold to say the least. Without linguistics, CS will be nowhere.

    Your focus on maths is pretty funny, almost a carricature. Maths, for other sciences, is ONE of the CONCEPTUAL TOOLS to understand these science. Maths are NOT the science.
    For example, in Geosciences, a domain I know particulary well, we can use pretty complex mathematics (in geophysics, the processing and interpretation of the signal is pretty complex and computer intensive) but it requires a lot of sense, intuition and 3D vision from the geologist. A geologist is a practician more than a scientist, our job is based on very undirect evidence, our judgements are always attached to uncertainties and risks. Computer technology is helping a lot for that. But the nicest part of the job is to find a new idea, a new domain, a new concept, understand a situation. This is not the maths which understand.

    It's your head.

    You have to put in your head that maths, although very useful, are totally useless without knowledge, experience, intuition (I keep hammering on this word) and a lot of other skills. A pure mathematician will not succeed even in physics.

    And what to say about the current (worrysome for some scientists) development in Symbolic Regression ? Using a "dirty" statistics genetic algorithm (based on the evolution totally a-mathematic theory of Darwin) and historic empirical data, symbolic regression is able to retrieve/discover equations.

    I wrote a paper last year for example where I demonstrated that this algorithm was able to rediscover the formula of Belotti and refined it linking acoustic impedance and rock density. What does it shows ? That the scientists can now use computer to assume a part of the mathematics of his job. It does not remove the thinking, to the contrary, it allows to focus better.

    Finally why referring to others ? Because you will quickly discover that your "fantastic new idea" have been explored 5-10 years ago and that you are using the ideas of others to build your own concepts.
    And the more you are trying to stay monoscience, the more you will be faced to this situation. Your ideas might be new when you will move to cross-domain.

    Cognitive psychology is good example. Without using complex mathematical models, it has introduced new concepts which cross-fertilized mathematics and statistics and then, it went back again to psychology.
    Geology is also a fantastic platform for cross domain applications.

    Just try to be much more open minded. Presently with such a mind set, you are just a predefined labs assistant, sorry to be so harsh.
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    Lee
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    You claim linguistics is so important to CS. I don't really see it. How can linguistics possibly be so important? CS has deep, abstract theory at the intersection between itself and math, more practical stuff like programs, etc. But I only really see a use for linguistics in stuff like speech recognition.
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    CS has deep, abstract theory at the intersection between itself and math

    Sorry, one quick point to be noted. If you want to study Maths and/or Computer "Sciences" (Technology for me), you have to first learn logic.

    Try to explain how something can be at the intersection between itself and something else .

    If A Λ B = A, then B = Φ

    (Sorry, i don't have all the symbol on my computer)
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    Lee
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    Actually, that was just my laziness with language there. But I guess I couldm odify my statement a bit. CS is a field full of many different things. From what I've seen, many people think that once either physics or CS get abstract enough, you're pretty much where the line begins to blur. How exactly this works I'm not aware of.

    But as CS as a field is a compartmentalization of several different concepts, if we look at just the more applied parts of CS at itself then we can posit this as "itself" and the definitely mathematical math as "something else", whereas thet stuff in the middle is where the line blurs, where it becomes more unclear whether its purely math or CS.

    Hope that clears up things a bit more.


    Anyways, anyone got any more information to share? Explanations, for example of why exactly linguistics matters in CS?
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    If you consider that CS is programming code like cattles are dropping dung, definetly, you won't need linguistics skills.

    However, at a higher domain, there has been cross-fertilization between li nguistic and Information Technology (which is a term I prefer). As I said before, working in silos is not helping science to move ahead. Most progress are due to bridges built between various domains.
    First, the development of linguistics helped to formalize the various programming languages. These languages are using very much the discovery of construction grammar.
    Second, the AI natural language processing for example are borrowing as well progress from linguistics.
    Third, although we are moving out of linguistics s.s., semiotics have helped to develop intelligent software.
    If that is not enough....
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  11. #10  
    Lee
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    Now that's what I'm talking about! That's a ton of interesting info right there... Considering we only came down to something really specific by talking about something really specific, I'll continue to wield that strategy in the future.

    Then, I'm might as well start now. Experimental or theoretical physics? I've read that a generation of string theorists are retiring, without any of their theories ever having been tested by experimentalists. Seems pretty horrible, and that's a definite notch-down for me.

    On the other hand, what type of problems can an experimentalist hope to solve? Don't they just run experiments and tinker with machines to test the theories of theorists?

    Second question, considering string theorists are retiring without testing their stuff, does that make for a huge red flag for high energy and other very abstract elitist physics stuff? Sounds like it would be better to do more manageable-scale problems so that whatever theories one comes up with (I assume the research in question is a theorist here) can actually be confirmed right or wrong within a realistic timeframe.

    Really, what kept those string theorists who are now retiring going for so many years anyways? Are they so dead sure of their own intuitions that they can just keep working, even when their theories may be totally wrong? It just sounds bad really. With all this made up junk, even though yeah sure making patterns and theories up can be fun as way of intellectual wanking, one still can't really know if one is actually reaching new levels of insight or not. It feels bad, knowing I might be just deluding my self. You may tell me to just keep to my fantasies exclusively - but no. I think happiness comes from both the material and mental realm. Also, of course, the fact that made-up useless **** won't be useful or sustainable in the long run, not a problem if you can keep the scam going for long enough to retire, but that risk's not worth it imo.

    Also, does any other science field have scandals of such scale as the one I listed above?



    I still do have a few more questions who are more towards to the vague side, so I'll ask them as well, in hopes of getting a few replies here and there. A question along the same line as the one about abstractness: What field of science would overall be the most complex, and which ten (If you can list that many) subfield in math, physics and CS would be the most complex in this day and age? If any of this is looking to or has historically changed fast, then I would be happy if you could include a bit about that in your post as well. If you can provide list of the most complex subfields in sciences other than CS, physics and math then that would be good as well. I'm also interested in which field have a solid amount of both complexity and abstractness, and which have little of neither. How are things looking to change in the future, will any fields overtake others while other are left in the dust? Historical facts and views are also interesting for perspective.

    Another question along those very same lines: Which sciences are more affected by epistemological problems. Economics seems like one, and I have a hard time taking it seriously. Seems like a bandwagon for predator capitalists to justify their moral wrongdoings. =/ I've heard neuroscience as well. Thoughts?

    And yet another one of those questions. Which sciences are cutthroat? As stated in an earlier post, I got the impression that life sciences overall are much worse with plagiarism and backstabbing.
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  12. #11  
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    The life sciences are cutthroat, a doctoral student I know had his article pre-empted by another team that realized they had done the same research when talking to his supervisor at a conference. He wasn't happy, it was practically career wrecking for a PhD student.

    On the other hand, the life sciences are cutthroat because there's money in life science research, big money. The life sciences also tend to dominate the high impact journals.

    Don't make the mistake of thinking any scientist are not cutthroat, most scientist are careerist with families to feed, they're all out to secure their livelihood. The altruistic scientist in it just for the science is a bit naive. Scientist often aren't looking to be rich, but they aren't looking to be unemployed either.
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    According to a friend of mine (Maths Doctorate), the number theory is probably the most difficult and theorical part of maths. And funny enough, it has tons of practical application, my friend was working for a secret service in cryptology during his younger age. He told me the number has a lot of fallout in CS (file compression, encoding, crypting etc...).
    In Computer Sciences, here is a partial copy of an article of Computer World:

    1. Analyzing Data
    By 2020, the amount of data generated each year will reach 35 zettabytes, or about 35 million petabytes, according to market researcher IDC. That's enough data to fill a stack of DVDs reaching from the Earth to the moon and back, according to John Gantz, chief research officer at IDC.
    Demand will be high for IT workers with the ability to not only analyze dizzying amounts of data, but also work with business units to define what data is needed and where to get it.
    2. Understanding Risk
    Risk management skills will remain in high demand through 2020, says futurist David Pearce Snyder, especially at a time when business wrestles with growing IT complexity. Think of IT problems on the scale of BP's efforts to stop the Gulf of Mexico oil spill, or Toyota's work to correct sudden acceleration in some of its cars, Snyder says.
    3. Mastering Robotics
    Robots will have taken over more jobs by 2020, according to Joseph Coates, a consulting futurist in Washington. IT workers specializing in robotics will see job opportunities in all markets, he adds.
    4. Securing Information
    Since we're spending more and more time online, verifying users' identities and protecting privacy will be big challenges by 2020, because fewer interactions will be face-to-face, more personal information may be available online, and new technologies could make it easier to impersonate people, according to a report by PricewaterhouseCoopers. Teleworkers will also represent a larger portion of the workforce, opening up a slew of corporate security risks.
    5. Running the Network
    Network systems and data communications management will remain a top priority in 2020, but as companies steer away from adding to the payroll, they will turn to consultants to tell them how to be more productive and efficient, says Snyder, who follows predictions from the U.S. Bureau of Labor Statistics.


    Data analysis and risk management are linked together. I worked on a couple of application on that, using data mining of historical data to reduce risk and uncertainties and measure the risk.
    Another good example, I downloaded recently a small apps for my Android called WhenWho. It uses data mining to go into the call history of your phone and predict the next incoming or outgoing call, by odds or time. I believe there will be more and more applications like this to extract significant knowledge from the large volume of information. We will move from a reactive mode to a pro-active mode, generating more and more knowledge, KPIs etc...

    Most sciences are affected by ethical problems (I think this is what you call epistemological, epistemology being the study of the mechanism behind the acquisition of knowledge). For example, in geology, most of my decision are uncertain and might be affecting even the life of people.
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