It has been three months since we published our preprint, and it has now passed 500 downloads. That seems like a good occasion to write about how I ended up working on molecular simulations and scientific software after leaving a promising assistant professorship.
I have tried to write about that decision several times, but the drafts kept growing and none reached publication. There was always more to explain. This time, I will focus on what led to the decision and what came of it.
I would like to emphasize the two reasons that she mentioned why career guidance is so difficult in Croatian academia (translation mine):
Limited experience of the mentors: Many mentors rely on their own experience in a system they are part of and know very well, and outside of which they often cannot help us much (depending, of course, on the field and personal affinities).
The “academia-only” narrative: Continuing higher education and a doctorate seem to presuppose a postdoc position and a teaching career. In addition, a good number of academics I was (and still am) surrounded by are convinced that academia is the only place where you can be creative and flexible, which is not true and makes it difficult to explore other possibilities.
I thought quite a bit about both of these points, given that I am guiding Matea Turalija through her PhD journey and I am eager to help her with my acquired experience as much as I can. However, as Klara explained above, this is far from a trivial endeavor.
Over the last couple of weeks, there has been a lot of discussion about scientific research funding in the US. There are many changes, and it remains to be seen what will come out of it in the end. If some of the new policies stick around for a longer time, I have no doubt they will inspire discussions and considerations in the rest of the world, including Europe, as many of them in the US tend to do.
While science funding in the US is an important and interesting topic, I would like to say a couple of words regarding science funding that hits closer to home, namely in Croatia. Earlier this week, the Croatian Science Foundation (HrZZ) closed the project call for Installation Research ProjectsUIP-2025-02. This was the first project call of this type after 5 years, which is possibly the reason why the usual range of eligible scientists with a maximum of 7 years after obtaining the PhD degree was extended to a maximum of 10 years after it. The call still requires a minimum of 2 years of experience after the degree.
My employment as a research and teaching assistant at Faculty of Informatics and Digital Technologies (FIDIT for short), University of Rijeka (UniRi) ended last month with the expiration of the time-limited contract I had. This moment has marked almost two full years I spent in this institution and I think this is a good time to take a look back at everything that happened during that time. Inspired by therecentposts by the PI of my group, I decided to write my perspective on the time that I hope is just the beginning of my academic career.
This month we had Alumni Meeting 2023 at the Heidelberg Institute for Theoretical Studies, or HITS for short. I was very glad to attend this whole-day event and reconnect with my former colleagues as well as researcherscurrentlyworking in the area of computational biochemistry at HITS. After all, this is the place and the institution where I worked for more than half of my time as a postdoc, where I started regularly contributing code to GROMACS molecular dynamics simulator, and published some of my best papers.
Tough question, and the one that has been asked and answeredoverandover. The simplest answer is, of course, it depends on many factors.
As I started blogging at the end of my journey as a doctoral student, the topic of how I selected the field and ultimately decided to enroll in the postgraduate studies never really came up. In the following paragraphs, I will give a personal perspective on my Ph.D. endeavor. Just like other perspectives from doctors of not that kind, it is specific to the person in the situation, but parts of it might apply more broadly.
Today I vaguely remembered there was one occasion in 2006 or 2007 when some guy from the academia doing something with Java and Unicode posted on some mailing list related to the free and open-source software about a tool he was developing. What made it interesting was that the tool was open source, and he filed a patent on the algorithm.
But there is a story from the workshop which somehow remained untold, and I wanted to tell it at some point. One of the attendants, Valérie Vaissier, told me how she used proprietary quantum chemistry software during her Ph.D.; if I recall correctly, it was Gaussian. Eventually, she decided to learn CP2K and made the switch. She liked CP2K better than the proprietary software package because it is available free of charge, the reported bugs get fixed quicker, and the group of developers behind it is very enthusiastic about their work and open to outsiders who want to join the development.
Viewpoints are not detailed reviews of the topic, but instead, present the author's view on the state-of-the-art of a particular field.
The first of two articles stands for open source and open data. The article describes Quantum Chemical Program Exchange (QCPE), which was used in the 1980s and 1990s for the exchange of quantum chemistry codes between researchers and is roughly equivalent to the modern-day GitHub. The second of two articles questions the open-source software development practice, advocating the usage and development of proprietary software. I will dissect and counter some of the key points from the second article below.
In 2012 University of Rijeka became NVIDIAGPU Education Center (back then it was called CUDA Teaching Center). For non-techies: NVIDIA is a company producing graphical processors (GPUs), the computer chips that draw 3D graphics in games and the effects in modern movies. In the last couple of years, NVIDIA and other manufacturers allowed the usage of GPUs for general computations, so one can use them to do really fast multiplication of large matrices, finding paths in graphs, and other mathematical operations.
