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consists mainly of research within a large collaborative project focusing on biodiversity, interactions and dynamics in coastal ecosystems. The project seeks to investigate drivers of spatial and temporal
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statistical methods (e.g., HLM, SEM, non-linear dynamical systems) and programming skills (e.g., R, Python, MATLAB) as well as data mining techniques for analyzing performance outcomes, self-report measures
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mathematical models to analyze and optimize waste flows. Optimization Techniques: Familiarity with advanced optimization techniques, including linear, non-linear, multi-objective, or stochastic optimization
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-Gurevich Probability, random walks, random graphs, probabalistic algorithms Genadi Levin Complex and one-dimensional dynamics, non-linear phenomena Noam Lifshitz Extremal combinatorics, Additive
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, probabilistic models Representation learning, self-supervised learning, foundation models Data analysis, non-linear statistics, knowledge management Your profile PhD in Computer Science, Bioinformatics
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. in chemistry, physics, or related fields • Experience in performing condensed-phase electronic structure calculations and molecular dynamics simulations • Strong communication skills in English
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technical and non-technical audiences Strong expertise in research design Ability to navigate and contribute effectively in a collaborative and dynamic research environment with evolving scientific priorities
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sets (dynamical system reconstruction), and apply linear control theory to investigate the energy demands of the resulting network architecture. This project will support our goal of testing
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modeling, engineering or a related discipline is required; expertise with linear control theory and dynamical systems theory are preferred but not necessary. You hold a doctoral degree or a diploma or