Dream job post

I was looking at job offerings and came across a post for a neurobiophysicist. Because it contained both the roots “neuro” and “biophysics,” my interest was piqued, since I’ve never heard of anyone referred to by this compounded title. It turns out that I’m apparently training to be a neurobiophysicist, and despite the location of the position (San Antonio, Texas), I am very intrigued by the job post. My hope is that other academic places are looking for someone who fits this description (emphasis added):

We seek to recruit a vigorous academic with an established track-record of research in the broad area of the physics of neurobiological processes. This might be anyone from a “patch clamper” to a computational biophysicist to someone studying signal transduction mechanisms but not necessarily limited to these examples. Trinity University has secured funds to endow the position and provide continuing funding for the position after our HHMI award expires. This professorship will be at the level of full professor. The position comes with a dedicated half-time support staff position, discretionary funds, and a reduced teaching load (6 rather than 9 contact hours per semester).

This position will be an appointment in the Department of Physics, providing some support to introductory physics courses, a course in biophysics (suitable for both physics and neuroscience majors), and a course in the faculty member’s specific area of expertise (specifically supporting neuroscience majors) in the teaching repertoire. In addition, the person hired in the position will maintain a vigorous research program that will engage undergraduate student researchers. The neuroscience major requires independent research as a culminating experience for the degree. Further details about the position are available in the position announcement.

The position will also work with faculty from biology, chemistry, and psychology in supporting the neuroscience major. This interdisciplinary major was established in 2005 with funding from our 2004 HHMI award. Through a combination and retirement and this HHMI-funded neurobiophysicist position, the neuroscience program will be expanded and redefined. In addition to this position, we will be hiring a cognitive scientist in Psychology and an animal behaviorist in Biology to support the neuroscience program.

It’s so accurately describes what I am interested in (and doing) because of aspects of combining computational approaches with electrophysiological techniques. Additionally, the appointment is in a physics department (my undergraduate degree is in physics), with teaching duties in both neuroscience and physics. I’m pretty far from doing a job search, but here’s to hoping positions like this become more common by the time I graduate!

The career plan (revised)

When I began my graduate educational journey, which started with a dead end job working in IT for a small business, I didn’t really have a good concept of how things might unfold. I did all of my applications with the not-so-professional guidance of close friends who had as much experience as I did in successfully applying to neuroscience graduate schools (none). When I was fortunate enough to have a choice between graduate programs, I had the luxury of choosing a research direction, between the genetically minded approach of one school to the computationally minded approach of the other.

I ultimately chose the computational direction because while clearly genetics is relevant now and will be forever relevant, I think that the coming of age of computational neuroscience is now. Genetics presents an ethical minefield that threatens to ultimately slow the scope of its reach (for better or worse), and I think this will delay the onset of the true age of genetic “understanding” with respect to nervous systems.

Additionally, another branch point came during rotations in my first years in graduate school, when I had some experimental opportunities to weigh against learning computational techniques. In the end I chose the mathematics, though, because I feel like these are techniques that are far more difficult to learn on one’s own. The anecdotal evidence for this has been realized time and again by experimentalists who are struggling to learn modeling techniques in a meaningful way. In contrast, I’ve seen a number of computational folks who have made the transition to experimental techniques seamlessly.

My plan thus far was to continue developing my computational techniques throughout grad school and then transition into a postdoctoral position and learn some electrophysiology. However, a phenomenal opportunity arose in which I might be able to do in vivo electrophysiology now, as part of my dissertation. I jumped at it, almost without proper or deliberate consideration. It sounds reasonable enough to me, though it obviously is accompanied by a bit of trepidation, considering the magnitude of this change.

I already have experience in biophysical modeling, and I’m working on some data analysis techniques which will serve me well. If I do electrophysiology now, then I can devote my postdoctoral appointment to different computational techniques, or perhaps a more mathematical project. Since the landscape of computational neuro is in a sense just as large as experimental neuro, there are far more techniques I have no experience with but am interested in learning. Once again, at this stage it’s unclear which approach will be more fruitful, but here’s to the uncertainty of the journey!

Fair assessment of students

In many disciplines, grading is arbitrary. We have all had the experience of having a professor or a teacher whose grading scheme was a black box. This is an archaic problem that has been passed on through generations of traditional educational models. Transparency in evaluation of student work is an important tenet of my teaching best practices.

Assessment of certain assignments lend themselves very well to objective criteria. In most multiple choice exams, the answer’s usually clear. The discrete nature of right and wrong lends itself well to grading. But as we all know, multiple choice exams aren’t necessarily the best way of assessing student learning (also not necessarily the worst). Even short answer questions in biology or multi step physics problems can introduce potential qualitative judgments that an instructor needs to make. Do I give credit for an answer that does not show work? How much “work” is enough for credit? Was the answer precise enough? In most of these assessment methods, a gradient of answer quality exists.

It is the duty of the instructor to be impartial to the students. In addition, I consider assessment to be an ongoing conversation between students and teachers, and in every classroom situation I’ve been in, I’ve tried to foster that environment. This is particularly difficult in an educational system that is structured around GPAs and grades, with students who have worked not to maximize their understanding but often have learned only how to optimize their grade with a Least Effort Approximation.

It is imperative for teachers to be open with their students about what they are looking for. The standards should be loose enough to allow for students to show the quality of their learning on any given task. In other words, tasks should be meaningful. Students will understand expectations better, and teachers are able to explain grades clearly when these guidelines are set. Controversy will undoubtedly exist, even with transparency, but at least everyone will be arguing on similar grounds, which unfortunately does not occur with most methods of evaluation.

If nothing else, teachers should be aware of the problem created by arbitrary assessment and opacity. I doubt I’ll be able to ever fully convince “old model” teachers to ever fully embrace openness with students, but that problem is independent of the arbitrary grading problem, which is a serious problem that probably affects most teachers — and all of their students.

Yet another argument on creation(ism)

All of this was prompted by an article in the Guardian on creation that I found particularly well written in a sea of poor discourse on the subject.

I am convinced that nearly everything meaningful to be said about the argument of creationism, intelligent design (ID), and evolution has been said. My position from a scientist and Christian’s point of view is straightforward. Obviously, there is a lot of evidence that exists regarding evolution. With respect to origins of the universe, there is also a surprisingly consistent amount of evidence for the big bang theory. So, my current belief, based on the evidence available, is that the big bang happened, and separately science currently paints a reasonable picture of the origin of life.

Now there’s something very intuitive about the idea that something had to precede the big bang, and that’s fundamentally what I’ll call God. (Call it what you like.) So, the event of creation, in my mind, occurred, but it’s not God placing tiny Lego humans, in their modern forms (whatever that is), on a pre-formed Earth. Creation was the release of energy from an infinitesimally small point into the expanding universe.

So that explains my theism. I am particularly Christian because I believe the fundamental story of Christ, based on all available evidence (Biblical and extra-Biblical accounts). My understanding of the canonization of the New Testament makes me believe that the Bible is more of a human document than it is handed down from God. While I’m much more inclined to take the Hebrew Bible on faith, it remains a question of faith and not science, though I’m pleased to see scientific and archaeological inquiries into the subject. Yet many of these fundamental beliefs I take on faith, and many of these questions cannot be addressed by science directly. It’s perhaps in my nature, however, to attempt to reconcile these two logical worlds in order to ensure that some sort of weird singularity doesn’t implode my head.

The theory of evolution, on the other hand, asks questions that are within the realm of science and testable science. We as scientists would be remiss if we did not admit that explicit statements about the past may well not be testable, but our observations are still meaningful, like a forensic puzzle, and we have the unique opportunity of having systems we can closely monitor in labs and in real environments to check consistency with current ideas.

For one broad example, biological conservation is striking. The preservation of even single, complex ion channels is maintained throughout species whose brains are vastly different. Just one of many, many examples of this is the human 6 transmembrane domain K⁺ channel herg, which is 70% similar in genetic sequence to a channel in the worm C. elegans and also similar to channels in the Drosophila fly and elk.

Modern evolutionary theory accounts for this. At least one alternative explanation to evolution that one might hear from the ID folks is that God could have placed these sequences in each species when God created them. This is not a testable hypothesis, and it is not scientific by definition. As far as science is concerned, until this is reconciled, end of discussion with respect to science!

With respect to education policy, and I probably have more to say at a later time on this issue, all of this sums up to the following. Evolution is a theory (like ALL other theories in science). It is not proven (like all other ideas in science). Evolution and big bang theories should be presented as a theory in science curricula. ID and creationism, on the other hand, have zero place, whatsoever, in a science curriculum. What could be explained is why this is, since it is such a curiously heated topic. As far as this scientist and Christian is concerned, it’s pretty binary.