Wissenschaftliche/r Mitarbeiter/in im Bereich "Hybrid AI models for control of cancer plasticity" (m/w/d)
About us
The chair of Prof. Bajcinca focuses on research of modern methods and advanced applications of control and system theory, involving three main pillars: cyber-physical systems, complex dynamical systems and machine learning. Through networking with a large number of national and international research, academic and industrial partners, funding projects with exotic and highly interesting tasks regarding model-based and data-driven control have been acquired on a regular basis. The research work is supported with an excellent laboratory equipment and high-performance computation in the areas of autonomous systems, robotics and energy systems, which is continuously being further developed.
https://www.mv.uni-kl.de/mec/home.
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
- A master's or Ph.D. degree in electrical engineering, power systems engineering, control systems engineering, or a related field. Ph.D. candidates may be preferred for more advanced research roles.
- Strong programming skills in languages like Matlab, C/C++, Python, or similar languages used for control algorithm development and implementation.
- Knowledge of real-time simulation platforms (dSPACE, Opal-RT), including PowerHIL systems or other hardware-in-the-loop testing environments.
- Familiarity with power system control theory, grid operation, and relevant industry standards.
- Good understanding of digital signal processing and control algorithms.
- Prior experience in conducting research related to real-time control of power systems or hardware-in-the-loop testing is highly desirable.
- Strong analytical and problem-solving skills to design experiments, collect data, and analyze results.
- Strong organizational skills and the ability to manage research projects, including experimental design, data collection, and project timelines.
- Proficiency in English or / and German is essential
- Highly motivated, eager to work within a team or independently.
We offer
- Payment according to TV-L E13 with an initial one-year time limit
- The possibility to do a PhD and to teach is given in case of scientific aptitude
- TUK strongly encourages qualified female academics to apply
- Severely disabled persons will be given preference in the case of appropriate suitability (please enclose proof)
- Electronic application is preferred. Please attach only one coherent PDF.
You can expect an interesting, diversified and responsible task within a young, highly motivated and interdisciplinary team of a growing chair with great personal creativity freedom.
Contact
Prof. Dr.-Ing. Naim Bajcinca
Phone: +49 (0)631/205-3230
Mobile: +49 (0)172/614-8209
Fax: +49 (0)631/205-4201
Email: mec-apps(at)mv.uni-kl.de
Keywords
Stochastic control
Random dynamical systems
Reinforcement learning
PDEs
Cancer plasticity
Application Papers
Cover Letter
CV
University Certificates
References
List of Publications
Application Deadline
15. April 2024
We will process your application as soon as received.
Job Availability
Immediate