Dec 27 2009

Grant Applications II

This post is probably not for you, unless you’re interested in grant applications!

I touched on grant applications a few posts ago with reference to the THES debate on blue-skies research, I mentioned my abysmal grant application record, the generally low success rate and the pain involved for all concerned. Here I intend to add a few additional comments arising, in part, from my experience in industry.

It’s worth stating what I believe the grant application process is for: on the face of it is a method by which discretionary funding is provided to researchers to provide resources for research; that is to say equipment, consumables and personnel. However, in addition to this it has a hidden purpose in that it is felt by many to be part of an rating process for researchers. Researchers believe that the more grant applications they win, the higher their ranking. Therefore top-down attempts to limit the number of applications a researcher can make cause consternation because they impact on the perceived worth of that researcher. This additional function is not explicit, and in a way it arises for a lack of any better measure of apparent researcher worth.

I believe this perception arises because university departments don’t do a very good job of career management for academics. As an employee of a very large company, I have regular discussions about where my career within the company is going – indeed in my first year I spent about an hour and half talking about just this subject, whilst in academia I *never* in 8 years post-doctoral employment, had a formal discussion about my career development. This applies both to those who have successfully made it to permanent lecturing positions, and the many post-doctoral research assistants who aspire to a limited number of permanent posts.

The grant application process takes no account of an attempt to create a wider research program. Grant applications are made to acquire a specific piece of equipment and/or someone to carry out the research proposed. Typically the equipment will be used long after the end of the grant, and there will be no formal mechanism of replacement.

I am still involved in writing internal research proposals, these differ in two ways from grant applications. Firstly, they are much shorter than grant applications – a couple of sides of A4; secondly, they are much more concerned with all the things ‘around’ the core of the proposal rather than an explicit description of the research to be done. Funding and allocation of resources is made at the level of projects comprising of order 10 or more people, rather than at the 1 or 2 researcher level at which the typical grant application aims. Furthermore there is a longer cascade in the resource allocation process, rather than each ‘end user’ approaching the holder of a central pot, resources are allocated at a higher level. This reduces the number of people in the grant application business and means that rounds of allocation are smaller affairs.

The winning of grants appears to contain a large element of lottery, that is to say the outcome depends to a moderate degree on chance. To improve your chances of winning a lottery, you buy more tickets. This has caused the EPSRC, at least, problems since although the amount available for grants has increased, the amount applied for has increased more rapidly.

There are two solutions to the problem of researcher disillusionment through the low success rate of grant applications, one is to increase the amount of cash available (which is unlikely to happen in the current economic climate), the other is to reduce the number of grant applications made – here the problem is how to do this in an equitable fashion. Part of the problem here is that the number of potential researchers is governed by the number of people required to teach the undergraduates population, rather than a judgement on the number of people required to consume the research allocation pot.

So what does this suggest for the grant application process:
1. Better career management for academics, in order that the grant application process is not used as a rating tool for academics;
2. Devolution of spending to a lower level;
3. More thought paid to providing continuity.

I guess in my ideal world an academic will develop a coherent, over-arching research plan which is executed in pieces by application to research funds at something like the university scale. The success of such applications depends largely on past performance, and on the coherence or otherwise of the over-arching research plan rather than an attempt to evaluate the quality of a particular piece of research, or idea, in advance.

It’s worth noting that academic research is seriously difficult, in that your ideas should be globally competitive – you should be developing thoughts about how nature operates at that are unique. Your competition is thousands of other, very clever researchers spread across the world. Compared to this, my job as an industrial researcher is easier – I need to communicate the answer to the question at hand to the appropriate person, if the answer already exists then that’s fine. Also I get to do more research with my own hands than I would in an equivalent position as an academic.

Dec 23 2009

What kind of scientist am I?

Following on from my earlier blog post on the tree of life, this post is about the taxonomy of my area of science: physics. I should point out now that I’m not too keen on the division science in this way. These divisions are relatively recent, as an example: the Cavendish Laboratory, the department of physics at Cambridge University, was only founded in 1874.

I am an experimental soft-matter physicist.

So taking the first word: experimental. This is one of the three great kingdoms of physics, the others being  computer simulation and the theory. “Experimental” means I spend a large part of my time trying to do actually experiments on objects in the real world, this may involve substantial computational work to process the output data and should generally involve some comparison to theory when published, although serious development of theory tends to end up in the hands of specialists. Computer simulation is distinct from from theory: simulation is like doing an experiment in a computer – give a set of entities some rules to live by and set them at it, measure results after some time. Theory on the other hand attempts to model the measurements without the fuss of explicitly modelling each entity in the collection.

Next to the physicist bit: In a sense theory is the essence of what physics is about: building an accurate model of the world. The important thing with physics is abstraction, to take an example I’m interested in granular materials; from a physics point of view this means I’m looking for a model that covers piles of ball bearings, avalanches, sand dunes, grain in silos, cereals in a box and possibly even mayonnaise all in a single framework.

And so to the final division: soft-matter. Physical Review Letters, which is the global house journal for physics, has the following subdivisions (in italics):

  • General Physics: Statistical and Quantum Mechanics, Quantum Information, etc; Domain of Schrödingers cat, Alice and Bob exchanging secure messages, and Bose-Einstein condensates.
  • Gravitation and Astrophysics; Physicists go large. Stephen Hawking lives here – black holes, the big bang.
  • Elementary Particles and Fields; down to the bottom, with things very small studied by things very large (like the Large Hadron Collider at CERN). Here be Prof Brian Cox.
  • Nuclear Physics; The properties of the atomic nucleus, including radioactivity, fission and fusion. This is Jim Al-Khalili‘s field. 
  • Atomic, Molecular, and Optical Physics; Stuff where single atoms and molecules are important, things like spectroscopy, fluorescence and luminescence go here.
  • Nonlinear Dynamics, Fluid Dynamics, Classical Optics, etc; Pendulums attached to pendulums, splashes and invisibility cloaks!
  • Plasma and Beam Physics; Matter in extreme conditions of temperature: fusion power goes here.
  • Condensed Matter: Structure, etc; Condensed matter is stuff which isn’t a gas – i.e. liquids and solids, and is acting in a reasonable size lump. 
  • Condensed Matter: Electronic Properties, etc; This is where your semiconductors, from which computer chips are made, live. 
  • Soft Matter, Biological, and Interdisciplinary Physics; Soft-matter refers to various squishy things, plastics, big stringy molecules in solution (polymers), little particles (colloids, like emulsion paint or mayonnaise), liquid crystals, and also granular materials (gravel, grain, sand and so forth).

So there I am in the last division, studying squishy things.

Since I’ve provided a means to wind up most sorts of scientist in previous blog posts, I thought I could provide a few here for me. Theoreticians can wind me up by assuming that experiments, and the analysis of the resulting data, are trivially easy to do and if they don’t fit their theory then I need to try again. Simulators I have a bit more sympathy with, simulations are experiments on a computer, however when you’re writing a paper perhaps you should say in the title you ran a simulation, rather than did a  proper experiment like a real man ;-)

Update: I made this post into a podcast: http://bit.ly/6EA17H – it’s on Posterous because uploading of audio is easier. I used a basic Logitech headset microphone, Audacity to do the capture and editing with the Lame plugin for MP3 export.  I’m not sure I’ll do it again but it was fun to try!

Dec 20 2009

Happy Christmas

 I hope you enjoy a happy week of eating, drinking, playing with friends and family, and looking at pretty lights.

Dec 17 2009

The Professionals

Lecturing is a tough business, and half the job is largely ignored.

This post is stimulated, in part by an article in Physics World on the training of physicists for lecturing, and how they really don’t like it. It turns out it is rather timely since Times Higher Education has also published on the subject, in this case highlighting how universities place little emphasis on the importance of good teaching in promotion.

I taught physics at Cambridge University: small group tutorials and lab classes – I was a little short of a lecturer. I also taught physics as a lecturer at UMIST. I should point out that the following comments are general, I think they would apply equally to any of the older universities.

Mrs SomeBeans is a lecturer in further and higher education, the difference between the two of us is that she had to do a PGCE qualification whereas I was let loose on students with close to zero training.

I did spend an interesting day in lecturer training at Cambridge, a small group of new lecturers, and similar, spent a fairly pleasant day chatting and being video’d presenting short chunks of lectures. I learnt several things on that day:
1. Philosophy lecturers use hardly any overheads.
2. Most of us found lecturing pretty nerve-wracking, one of our number wrote out her lectures in full in longhand to cope.
3. Drinking as a cure for pre-lecture nerves doesn’t work well
4. I spoke like a yokel and was slightly tubbier than I thought!

Round two at my next employers was a bit more involved. I can’t remember much from the two day event, but many of the points from the Physics World post came out. Scientists are typically taught how to lecture together as group, and their point of view is somewhat in collision with those of educationalists who seem to be able to throw out three mutually incompatible theories before breakfast and not be interested in testing any of them.

I have an insight which may help scientists in these situations: outside science the idea of a “theory” has quite a different meaning from that inside science. This paradox is also found in management training. Non-scientists use a “theory” as a device to structure thought and discussion, not as a testable hypothesis. Therefore multiple contradictory, or apparently incompatible theories, can be presented together without the speaker’s head exploding. They’re not generally tested in any sense a scientist would understand, very few people attempt to quantify teaching Method A against teaching Method B. The thing is not to get hung up on the details of the theory, the important bit is being brought together to talk about teaching.

I enjoyed parts of teaching: physics tutorials for second years at Cambridge was something of a steeplechase with the not particularly experienced me, hotly pursued by rather cleverer undergraduate students over problems for which the lecturers did not deign to supply model answers. Exceedingly educational for all concerned. Practical classes were also fun: the first time a student presents you with a bird’s nest of wires on a circuit board it takes about 15 minutes to work out what the problem is, the second time you immediately spot the power isn’t connected to the chip – and students think Dr Hopkinson is a genius.

Lecturing I found pretty grim, except on the odd good day when I got an interesting demonstration working. I was faced with 80 or so students, many of an unresponsive kind. I ploughed through lecture notes on PowerPoint which I found interesting when I was writing but in the lecture theatre I found painfully long winded. Lecturing is the most nerve-wracking sort of public speaking I’ve done, and I suspect many lecturers find it the same. I remember one of my undergraduate lecturers was clearly a bag of nerves even in front of the small and friendly course to which I belonged (and I’m not good at picking up such things).

In a sense lecturing is a throwback, there are so many other ways to learn – and I fear we only teach via lecturing because that’s what we’ve always done. Nowadays it’s easy, although time consuming, to produce a beautiful set of printed lecture notes and distribute the overheads you use: but is it really a good use of time to go through those overheads (which I am sure is what nearly everyone does)? Nowadays I learn by reading, processing and writing (a blog post) or a program.

There’s another thing in Physics World article:

At universities the task is often performed by academics who are much more interested in research and therefore regard teaching as a chore.

This is absolutely true, in my experience. I’ve worked in three universities post-undergraduate, I’ve been interviewed for lectureships in a further six or so. And in everyone the priority has been research not teaching, which is odd because if you look at funding from the Department for Innovation, Universities and Skills something like £12billion is directed at teaching and something like £5bn at research.

So why did I write this post: perhaps it’s a reflection of opportunities missed and a time spent chasing the wrong goals. If I did it all again there seem to be so many more ways to talk to other lecturers about teaching. On twitter, in blogs.

Dec 13 2009

Drowning by numbers

I am not a mathematician, but physics and maths are intimately entwined. I suspect I stumble on a deep philosophical question when I ponder whether maths exists that has no physical meaning.

On a global scale I am moderately good at maths, I have two A levels* in the subject (maths and further maths), long years of training in physics have introduced me to a bit more. However, beyond this point I realised I was manipulating symbols to achieve correct results rather than really knowing what was going on. A lot of my work involves carry out calculations, but that’s not maths.

I did intend decorating this post with equations, I didn’t in the end, wary of a couple of things: firstly the statement by Stephen Hawking that every equation would half sales; secondly I discovered that putting equations into Blogger is non-trivial. Equations, statements in mathematical notation are the core of maths and much of my journey in maths has been in translating equations into an internal language I understand.

So here’s a pretty bit of maths, the Mandelbrot set, the amazing thing about the Mandelbrot set is how easy it is to generate such a complex structure. We can zoom into any part of the structure below and see more and more detail. Mathematics is the study of why such a thing is as it is, rather than just how to make such a thing.

Image by Wolfgang Beyer

I remember playing with Mandelbrot sets as a child, before I understood complex numbers, to me they were a problem in programming and a source of wonder as I plunged ever deeper into a pattern that just kept developing. Have a look yourself with this applet… *time passes as I re-acquaint myself with an old friend*. There is something of this towards the end of Carl Sagan’s novel Contact, where the protagonists discover a message hidden deep within the digits of π.

I fiddle with numbers when I see them, and I suspect mathematicians do too. So my Girovend card showed 17.29 recently which, without the decimal place is 1729 = 13+123 = 103+93, the smallest number that has the property of being the sum of two different pairs of positive cubes, it’s also a very common piece of numerology. The numbering of the chapters of “The Curious Incident of the Dog in the Night-time” by Mark Haddon, with consecutive prime numbers also appeals to me.

It’s become a tradition that I find ways to annoy the people I visit, and there’s no escape for mathematicians here. It seems like the best way is to annoy a mathematician is to assume they can do useful arithmetic, like a calculating a shared restaurant bill. Interestingly though, this may be a poor example, since fair division methods for important things, like cake, are an area of mathematical research.

It’s true that some mathematicians are a bit odd, but then so are some physicists and to be honest if reality TV has taught us anything, it’s that the world is full of very odd people in every walk of life. So if you meet a mathematician, don’t be afraid!

*A levels are the qualification for 18 year olds in the UK, when I was a student you would study for 3 or 4 A levels for 2 years.

Update: Since writing this I’ve discovered a couple more interesting sites for fractals, and for want of a better place to put them I record them: here you can find a pretty rendering of the quaternion Julia set, and here is an in depth exploration of the Julia and Mandelbrot sets (1/1/10).