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Field
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: dr hab. Natalia Szóstak Research topic Prediction of response to immunotherapy in melanoma using metabolic signatures of the gut mycobiome. I. Project description
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05.07.2026, Academic staff Our research combines mathematical modeling, numerical simulation, scientific computing, and data-driven methodologies to improve the predictive capabilities and
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, physical and chemical conversion strategies and engineering processes (b) understanding and controlling their underlying specific functionalities and the structure-properties relationship acting over
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methods and predictive control strategies for solid-state batteries using data from parallel postdoctoral projects within the initiative. The research aims to improve the understanding of how operating
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AI-based decision-making, learning approaches, and predictive capabilities to enable spacecraft to react proactively to events while reducing reliance on ground intervention. A key focus will be
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analysis of metagenomic and viromic sequencing datasets generated through the laboratory’s bacteriophage research program. Primary responsibilities will include sequence quality control, metagenomic assembly
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validation, while establishing protocols for sensor calibration, spatial sampling, quality control and integration with Earth Observation data. Develop spatial, statistical and predictive models to investigate
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PHS tolerance under a controlled optimum temperature and relative humidity regime. ● (20%) Develop and validate a protocol to predict the risk of PHS under field conditions. ● (40%) Publish findings in
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and AI-based approaches, including predictive modeling, stratification, explainable AI, and integrative multimodal analysis. The scholar will have opportunities to lead first-author publications
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; survival or hazard models; gradient-boosted decision trees or related predictive approaches; interpretable feature-importance methods; Git/GitHub; Linux or command-line workflows; preregistered analyses; and