Our goal is simple – to be the best in our field. We work on meaningful projects which push the world forward.
Our current research topics are cybersecurity, cybercriminality, development of infrastructure, and other interdisciplinary projects such as cancer research.
What Can We Offer You?
Background of a significant scientific and research institution in the Czech Rep.
Research centres CERIT-SC, C4e and CSIRT-MU
Joining a team of experts
Possibility to combine work with university lecturing
Supervision of diploma thesis as well as doctoral students
Support of relocation for those moving from abroad
An array of work benefits
CSIRT-MU offers a research-oriented position for academics from Ukraine whose focus is on cybersecurity. Namely in the areas of network traffic monitoring and analysis, network security and intrusion detection, cybersecurity situational awareness.
It is a fixed-term contract with the possibility to prolongation (through R&D projects).
With us, You can work on:
- Current R&D projects of CSIRT-MU.
- Writing R&D project proposals.
- Your own research topic if it is related to the areas mentioned above.
- Supervision or co-supervision of master’s students, or participate in teaching courses provided by CSIRT-MU members.
Details will be settled upon further discussion.
What we expect from You:
- Ph.D. in computer science, with focus on cybersecurity, networking, or related areas
- Expert knowledge in at least one of the areas mentioned above
- Experience with collaborative projects with industry or R&D project leadership is an advantage
- Spoken and written english proficiency C1 (certificate not needed)
- Ability to produce independent scientific work and lead other junior researchers.
- Permanent residence in Ukraine within the past 3 months (this position is meant for academics from Ukraine affected by the ongoing war).
We offer you:
A workplace at the Masaryk University, and all the benefits that come with it.
Facilities of the Institute of Computer Science, a proud recipient of the HR Excellence in Research Award presented by the European commission.
Resources of CSIRT-MU, the first certified cybersecurity team in the Czech republic
Full or part-time employment. Monthly wage between 40 000 and 50 000 CZK (full-time) depending on your current level of experience and field of work.
Other work benefits including monthly team-builidng activities, 6 weeks of vacation, meal allowance.
Career growth to depending on your prior experience and area of expertise, as discussed during the official job interviews.
Does this offer sound interesting? Send us your CV and contribute to cutting edge cybersecurity research!
Centre CERIT-SC (CERIT Scientific Cloud), a national centre operating computing and data storage infrastructure for executing large-scale experiments “in-silico”, often collaborating with other scientific disciplines, is looking for a new postdoc researcher to join the team working on advanced machine learning models in mass spectrometry.
What is the project about?
Mass spectrometry is a widespread experimental technique used to identify chemicals in samples e.g. biological, environmental, etc. As with most state-of-the-art techniques, it is tightly coupled with complex computational processing of the acquired data. In routine usage, it is used to confirm a specific compound in the sample. Advanced usage identifies which millions of known compounds in databases are contained in the sample. The most challenging usage attempts to identify compounds that were not seen before and are not recorded in the databases.
In 2021 four independent works, which address this "de novo" identification challenge with state-of-the-art machine learning techniques, appeared (references below). They all share the approach of supervised training of an underlying neural network model using a huge set of mass spectra generated in-silico, achieving an even larger training set than direct use of existing spectral databases would allow. Afterwards, the models are refined with the available experimental data.
The neural network models are strongly inspired by natural language processing; they follow LSTM or transformer architectures.
From the application point of view, all the published techniques address the LC-[ESI]-MS2 (liquid chromatography, electrospray ionization with two-stage fragmentation) variant of the experimental technique, which yields slightly different data from the GC-[EI]-MS (gas chromatography, electron ionization with single-stage fragmentation) we use as our dominating experimental techniques. Therefore, computational processing is not directly applicable.
What would be the short term goals of this position?
In the first year, the candidate is expected to get familiar with the cited work in detail and reproduce the results of at least some of them. Then he/she will modify the selected method to be applicable in our setup. The expected outcome is, besides the working software and its proper evaluation with representative testing data, a journal publication submitted in a good shape.
What is the research team like?
The candidate will work in a small interdisciplinary team with the participation of senior researchers, PhD and undergraduate students joining with both chemistry and computing backgrounds. Some preliminary work has been done on this specific topic and some knowledge already built in the team.
What do we offer?
- Exciting research topic, following very recent results, with high application potential
- Opportunity to achieve outstanding results quickly, development of scientific career
- Well-established interdisciplinary team with friendly relationships
What do we require?
- PhD in computing or natural science (chemistry, biology, physics, ...) with a clear focus on computational aspects
- Experience with baseline machine learning frameworks (Tensorflow, PyTorch); further experience with advanced frameworks (HuggingFace, ...) is welcome
- Experience with large-scale computing (parallel computation, GPU acceleration, use of computing clusters/supercomputers)
- Willingness to work in the interdisciplinary team, i.e. also building minimal knowledge of the application area (mass spectrometry); prior knowledge is not required, though
- English: oral communication, reading, writing (approx. C1 level)
If you like what you've read, don't hesitate to email us your CV.
- Eleni Litsa et al., Spec2Mol: An end-to-end deep learning framework for translating MS/MS Spectra to de-novo molecules, https://chemrxiv.org/engage/chemrxiv/article-details/613e83a7656369203b2a249b
- Svetlana Kutuzova et al., Bi-modal Variational Autoencoders for Metabolite Identification Using Tandem Mass Spectrometry, https://www.biorxiv.org/content/10.1101/2021.08.03.454944v1.full
- A.D.Shrivastava et al., MassGenie: A Transformer-Based Deep Learning Method for Identifying Small Molecules from Their Mass Spectra, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8699281/
- M.A.Stravs, MSNovelist: De novo structure generation from mass spectra, https://www.biorxiv.org/content/10.1101/2021.07.06.450875v1.full
“A desire to solve new and demanding tasks lead me to research, and so I became focused on high performance computing. I’m partaking in software development which is applied in the advancement of medicaments, hence making my work much more meaningful. As a father of three children, I also appreciate the opportunity and flexibility to adjust this time-demanding performance to my personal needs.”Jiří Filipovič
Head of the research team in the field of High Performance Computing
Do you need any further information? Send an e-mail to firstname.lastname@example.org.