Dual Career

Im Rahmen des HiLusatia network weist unserer Kooperationspartner CASUS auf folgende Stellenangebote hin:

Jobs

  • Scientific Project Coordinator (f/m/d) Optimization of limited testing resource for SARS-CoV-2

    The Scope of Your Job

    The scientific project coordinator will be part of a team studying how to optimally deploy limited testing capacity in an emerging epidemic.

    This successful candidate will be responsible for coordinating activities across team members in the areas of data fusion, epidemiological modeling, algorithm development, and software development. Importantly, the team must be synchronized to produce near-term products that start simple and then are gradually expanded in scope, functionality, and refinement.

    The scientific project coordinator will also coordinate activities with project partners, both within Germany and internationally, and will facilitate interactions with public health professionals and institutions in Germany.

    Deadline

    Review of applications will begin on 24 August 2020, but the position will remain open until filled.

    Job Offer in English [PDF, 198 KB]

    Opis pracy po polsku [PDF, 182 KB]

Postdoctoral Researchers (f/m/d)

Rolling application – open until filled

  • Theory development for neutral biodiversity dynamics on dendritic networks

    The successful candidate will be part of a team studying how river network geometry and hydrology interact to shape freshwater fish biodiversity patterns in river systems worldwide.

    This position will focus on pursuing analytical solutions to neutral biodiversity models applied to dendritic networks.

    The postdoc will begin with an existing neutral model that has successfully been applied to the Mississippi-Missouri river system (Muneepeerakul et al. 2008), and explore strategies for solving the model analytically. Subsequently, the postdoc will expand the model to consider deviations from purely neutral dynamics including those generated by point sources of pollution and changes in flow control (e.g., dam removal or reservoir construction).

    The position requires advanced mathematical skills and experience in working with multivariate stochastic processes.

    Job Offer [PDF, 175 KB]

  • Data-intensive research on fish biodiversity in relation to river network geometry and hydrology

    The successful candidate will be part of a team studying how river network geometry and hydrology interact to shape freshwater fish biodiversity patterns in river systems worldwide.

    This position will focus on developing and analyzing an extensive database of spatially referenced fish species occurrence records. This database will be combined with existing data on hydrology and river geometry compiled by project collaborators and analyzed to identify key drivers of variation in fish biodiversity.

    This position requires advanced statistical, programming, and data management skills.

    Job Offer [PDF, 175 KB]

  • Global, multispecies comparative analyses of animal movement processes

    The successful candidate will be part of an established animal movement analytics research team.

    Building on a long-term research program in animal movement analytics (e.g., Fleming et al. 2015, Calabrese et al. 2016, Fleming & Calabrese 2017, Noonan et al. 2019), this position will focus on leveraging an existing multispecies tracking dataset to:

    1. understand species- and location-specific variation in animal movement processes, and
    2. compare and contrast alternative methods for quantifying animal space use.

    This position requires advanced statistical and programming skills.

    Job Offer [PDF, 189 KB]

  • Spatio-temporal epidemiological modeling of COVID-19

    Scope of Your Job

    The postdoctoral researcher will be part of a team studying how to optimally deploy limited testing capacity in an emerging epidemic.

    This position will focus on developing epidemiological models with at least county-level resolution, initially for the state of Saxony, and subsequently for all of Germany and possibly other countries. These models must account for interconnections among spatial units and thus must go beyond location-specific but otherwise independent models.

    The successful candidate will analyze the behavior of the models, couple them with extensive data compiled within the project and by project partners, and use the models to create scenarios that can be used as a backdrop to evaluate the performance of methods for optimizing limited testing resources in an emerging epidemic.

    Deadline

    Review of applications will begin on 24 August 2020, but the position will remain open until filled.

    Downloads

    Job Offer (English) [PDF, 206 KB]

    Opis pracy (polski) [PDF, 173 KB]

  • Data-intensive research on characterization of SARS-CoV-2 testing strategies and infection risk

    Scope of Your Job

    The successful candidate will be part of a team studying how to optimally deploy limited testing capacity in an emerging epidemic.

    This position will focus on developing an extensive database of factors correlated with individual risk of infection, such as age, occupation, spatial location, and contact networks. In parallel, the candidate will also build a database quantifying variation in testing rates and strategies, including locations of hospitals, clinics, and other SARS-CoV-2 testing sites in relation to demographic data such as population density and population age structure. These databases will first focus on Saxony, but can subsequently be expanded to the rest of Germany and other countries.

    Using the latest data science techniques, the successful candidate will analyses these databases to develop efficient proxies for characterizing individual infection risk, and will relate these to variation in testing rates and strategies.

    Deadline

    Review of applications will begin on 24 August 2020, but the position will remain open until filled.

    Downloads

    Job Offer (English) [PDF, 206 KB]

    Opis pracy (polski) [PDF, 174 KB]

  • Algorithm development for constrained spatio-temporal optimization of SARS-CoV-2 testing strategies

    Scope of Your Job

    The successful candidate will be part of a team studying how to optimally deploy limited testing capacity in an emerging epidemic.

    This position will focus on developing optimization algorithms for informing policies on how to most efficiently use limited testing resources in an emerging epidemic to accurately characterize spatial-temporal disease prevalence. These algorithms will leverage an extensive dataset on risk factors, health care services, and demographics, as well as parameterized spatio-temporal epidemiological models, both of which will be developed by other team members. The initial focus will be on the state of Saxony in Germany, with subsequent expansion to the rest of Germany and possibly other countries.

    Deadline

    Review of applications will begin on 24 August 2020, but the position will remain open until filled.

    Downloads

    Job Offer (English) [PDF, 200 KB]

    Opis pracy (polski) [PDF, 173 KB]

  • Webportal development for optimization of SARS-CoV-2 testing strategies

    Scope of Your Job

    The postdoctoral researcher will be part of a team studying how to optimally deploy limited testing capacity in an emerging epidemic.

    This position will focus on developing an open web platform for test strategy optimization and modeling. The initial focus will be on the state of Saxony, with subsequent expansion to the rest of Germany and possibly other countries.

    The successful candidate will work closely with other team members that will develop the datasets, models, and optimization algorithms underpinning the web platform. The web platform will provide end users with flexibility to define their own optimization goals. Importantly, the platform must be produced with a continuous development/deployment strategy to make early versions available as soon as possible, with subsequent versions building in scope and refinement.

    Deadline

    Review of applications will begin on 24 August 2020, but the position will remain open until filled.

    Downloads

    Job Offer (English) [PDF, 205 KB]

    Opis pracy (polski) [PDF, 172 KB]

PhD Students (f/m/d)

Rolling application – open until filled

  • Data-Driven Simulations of Tissue Dynamics in Developing Embryos

    CASUS's Systems Biology Department, in partnership with the Center for Systems Biology Dresden and the Federal Cluster of Excellence “Physics of Life” at TU Dresden, seek to understand living matter on the basis of physical principles.

    A physical principle that has been particularly successful in describing the behavior of active biological material out of equilibrium is the theory of Active Polar Gels. Traditionally, this theory is used in a top-down way starting from the conservation laws expressed as partial differential equations. While this was successful, it begs the question what the values and molecular meanings of the coefficients in the equations are.

    In this project, we address this fundamental question in a data-driven way. We exploit recent advances in learning mathematical models from data (https://arxiv.org/abs/1907.07810) in order to use microscopy videos of developing embryos to learn bottom-up physical models. These data-driven model will include the chemical regulation and the biomechanics of the tissue in an attempt to explain the self-organized emergence of shape and function during morphogenesis. Comparing these models with the top-down derived models will provide new insight into the mysterious physics of life.

    Job Offer [PDF, 196 KB]

  • Predictive Computer Simulations of Tissue Morphogenesis

    The question of morphogenesis has fascinated scientists since Darwin and D’Arcy Thomson. How does a living tissue, such as an embryo or an organ, develop into a well-defined shape? What regulates the emergence of shape? How is shape encoded in the genes? How is the great variety of shapes we observe in nature generated by genetic mutation? How is shape control robust against noise and environmental fluctuations?

    At CASUS, we address these fundamental question in close collaboration with the Center for Systems Biology Dresden and the Federal Cluster of Excellence “Physics of Life” at TU Dresden, seeking answers to a century-old question with obvious applications in regenerative medicine. A prerequisite for the studies, however, is the availability of a predictive computer simulation of models or morphogenesis in order to prove their sufficiency and screen for viable parameters.

    In this project, we will develop and numerical tools and implement the simulation software in the open-source parallel computing framework OpenFPM. The code should be able to simulate both data-driven and theory-driven models of tissue dynamics, including models of topological control of shape. The latter will be done in collaboration with CASUS’s materials science department, as similar concepts exists in the field of complex shape-programmable materials. In the end, you might just have the world’s first 3D simulation of biological morphogenesis.

    Job Offer [PDF, 209 KB]

  • Adaptive Multi-Scale Simulations on Parallel Computers

    A central aim of CASUS is to provide the scientific community en large with scalable, professionally maintained, and user-friendly software frameworks for complex scientific computing tasks. A particularly challenging task is the numerical solution of multi-scale models using self-adaptive numerical methods and sparse data structures that scale well on both GPUs and CPU clusters. State-of-the-art adaptive resolution codes mostly rely on Adaptive Mesh Refinement (AMR) or mesh-free methods using wavelet decompositions. Both pose severe scalability limits, AMR due to the global nature of the tree data structure, wavelets due to the global communication required and their log-linear computational complexity.

    In the present project, we will explore the use recent advances in adaptive sampling, the Adaptive Particle Representation (APR), in order to provide a linear-time self-adaptive numerical method with theoretically proven error bounds on the derivatives of the function. This will then be implemented in a community-driven open-source parallel computing framework in order to make it available to users and demonstrate accuracy and scalability.

    Job Offer [PDF, 209 KB]

  • Multidimensional Interpolation Algorithms and their Application in Scientific Computing

    The project is embedded in a larger collaboration with MPI-CBG and TUDs Department of Mathematics and aims to exploit recent advances in algebraic topology to provide novel algorithms for solving high-dimensional interpolation problems with vastly improved computational performance and accuracy.

    Interpolation is at the heart of many applications in scientific computing, from numerical solutions of differential equations, to deep learning. You will work with experts in the field of mathematics to further develop the algorithmic foundations, and with colleagues from the other CASUS Departments in order to apply the new algorithms.

    Applications include, but are not limited to, surrogate modeling for machine learning, environmental data science and ecology, and numerical solutions of 6-dimensional models in plasma physics. Another exciting direction includes the formal derivation of high-dimensional cubature formulas, and applications in discretizing active polar gel models for biological tissue dynamics.

    Job Offer [PDF, 212 KB]

Ansprechpartnerin

Dipl.-Kffr. (FH)
Heike Kallweit
Dezernat Personal und Recht
Campus 02763 Zittau
Theodor-Körner-Allee 16
Building Z I, Room 1.06.1
+49 3583 612-4492
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