Research Associate in "Hybrid AI models for control of cancer plasticity" (m/w/d)
Keywords: Stochastic control, Random dynamical systems, Reinforcement learning, PDEs, Cancer plasticity
Research Framework
Cancer is a complex and ever-evolving disease, exhibiting a remarkable ability to adapt and change over time, known as cancer plasticity. Despite substantial advancements in cancer research and treatment, cancer plasticity poses a significant challenge in our quest to combat this multifaceted adversary. To address this challenge, we are seeking motivated researchers to join our team in exploring the groundbreaking field of "Hybrid AI Models for Control of Cancer Plasticity." This research endeavor represents a critical step towards unraveling the mysteries of cancer plasticity and developing innovative strategies for prediction and control of cell transitions.
To tackle these challenges posed by cancer cells' plasticity, we must harness the power of cutting-edge technology. This is where the concept of "Hybrid AI Models" comes into play. Our motivation stems from the realization that traditional approaches to cancer research and treatment are often inadequate in dealing with the complexity of cancer plasticity. However, hybrid models have the potential to bridge the gap between traditional cancer research and the ever-evolving landscape of AI-driven insights.
Task Description
The research compiles from the following list of tasks.
- Developing hybrid mathematical models integrating AI methods, to decipher the intricate landscape of cancer plasticity.
- Utilizing high-dimensional data sources, including genomics, proteomics, and single-cell sequencing, to design and refine your models.
- Investigating the dynamics behind evolution of cancer plasticity landscapes and predicting cancer cells transition between states and adaptation to drugs.
- Collaborating closely with multidisciplinary teams, including biologists, mathematicians, and data scientists, to validate and refine your predictions using organoid models.
- Finding new strategies to predict cancer plasticity patterns, facilitating early intervention and personalized treatment approaches.
- Exploring AI-driven control mechanisms to guide cancer cells towards less aggressive states, ultimately improving patient outcomes.
Qualification
- Above average university degree in applied mathematics, biomathematics, control engineering, bioinformatics or related disciplines.
- Biological knowledge of cancer is expected.
- Knowledge in mathematical modeling and analysis is expected.
- Knowledge of at least one programming language: Matlab, Python, C++ is expected
- Proficiency in English or / and German is essential
- Highly motivated, eager to work within a team or independently.
Application procedure and deadline:
Applications must include the following elements (as a single PDF file):
- Cover letter with a brief description of why you want to pursue research studies, about what your academic interests are, and how they relate to your previous studies and future goals
- CV including your relevant professional experience and knowledge
- Copies of diplomas and grades from previous university studies
- Two references
- List of publications
Send an email with the required documents to the address: @mec-apps@mv.uni-kl.de. The application deadline is 28. February 2025.
Interested candidates are encouraged to apply promptly, as applications will be processed as received, and the position may be filled before the deadline.
For more information, visit our webpage: https://mv.rptu.de/fgs/mec/research
