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- Aufgaben: Dive into groundbreaking research on machine learning applications in cancer treatment.
- Arbeitgeber: Join the German Cancer Research Center, dedicated to a future without cancer.
- Mitarbeitervorteile: Enjoy flexible hours, 30 vacation days, and a family-friendly work environment.
- Warum dieser Job: Make a real impact in personalized oncology while collaborating with top researchers.
- Gewünschte Qualifikationen: Master's in computer science, bioinformatics, or related fields; strong machine learning knowledge required.
- Andere Informationen: Position limited to 3 years; applications accepted until January 30, 2025.
Das voraussichtliche Gehalt liegt zwischen 36000 - 60000 € pro Jahr.
„Research for a life without cancer“ is our mission at the German Cancer Research Center. We investigate how cancer develops, identify cancer risk factors and look for new cancer prevention strategies. We develop new methods with which tumors can be diagnosed more precisely and cancer patients can be treated more successfully. Every contribution counts – whether in research, administration or infrastructure. This is what makes our daily work so meaningful and exciting.
Together with university partners at seven renowned partner sites, we have established the German Cancer Consortium (DKTK).
For the Research Group „Machine Learning in Oncology“ (headed by Prof. Dr. Florian Buettner) at the DKTK partner site Frankfurt/Mainz and the Goethe University Frankfurt, we are seeking for the next possible date a PhD candidate.
The Buettner lab ( works on the intersection of machine learning and oncology and as such is actively pursuing original research in both areas. Your PhD research at the intersection of machine learning, genomics and oncology will explore how machine learning solutions can be used to accelerate progress in personalized oncology.
Your Tasks
Join us on an exciting new project aimed at understanding and modulating the tumor microenvironment (TME) in colorectal and pancreatic cancer via cutting-edge mRNA technology. Your PhD will focus on building and applying inherently interpretable deep probabilistic machine learning models for multimodal data integration. In collaboration with experimental and clinical partners from Mainz and Heidelberg, you will use these AI models to guide mRNA technology for modulating the TME, thereby helping to broaden the scope of mRNA technologies beyond its well-established application as vaccination. Through iterative cycles of data integration, hypothesis generation, and wet-lab feedback, you will help reveal key pathways that modulate therapy resistance and drive personalized immunological interventions.
Your Profile
We are looking for a candidate with a background in computer science, statistics, bioinformatics or a related field (e.g. master’s degree in mathematics, physics, computer/data science, computational biology or related). A good knowledge of machine learning methods and statistics is essential, as is an interest in biomedical applications and cancer research; familiarity with probabilistic modeling and Bayesian methods is highly desirable. Good knowledge of programming/scripting languages (e.g. R or Python, knowledge of both is a plus) and best practices in software development as well as experience with Linux environments are required. Experience with bioinformatics algorithms and applications is a plus. The candidate will closely interact with other researchers and clinicians, therefore good English communication skills are also required.
We Offer
Excellent framework conditions: state-of-the-art equipment and opportunities for international networking at the highest level
Access to international research networks
Doctoral salary with the usual social benefits
30 days of vacation per year
Flexible working hours
Possibility of mobile work and part-time work
Family-friendly working environment
Sustainable travel to work: subsidized Germany job ticket
Unleash your full potential: targeted training and mentoring through the DKFZ International PhD Program and DKFZ Career Service
Our Corporate Health Management Program offers a holistic approach to your well-being
Are you interested?
Then become part of the DKFZ and join us in contributing to a life without cancer!
Contact:
Prof. Dr. Florian Büttner
Phone: +49 173 4613687
Duration:
The position is initially limited to 3 years.
Application Deadline:
30.01.2025
Applications by e-mail cannot be accepted.
We are convinced that an innovative research and working environment thrives on the diversity of its employees. Therefore, we welcome applications from talented people, regardless of gender, cultural background, nationality, ethnicity, sexual identity, physical ability, religion and age. People with severe disabilities are given preference if they have the same aptitude.
Notice: We are subject to the regulations of the Infection Protection Act (IfSG). Therefore, all our employees must provide proof of immunity against measles.
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PhD Student for Machine Learning in Oncology Arbeitgeber: DKTK partner site Frankfurt/Mainz - Machine Learning in Oncology
Kontaktperson:
DKTK partner site Frankfurt/Mainz - Machine Learning in Oncology HR Team
StudySmarter Bewerbungstipps 🤫
So bekommst du den Job: PhD Student for Machine Learning in Oncology
✨Tip Number 1
Make sure to familiarize yourself with the latest advancements in machine learning applications in oncology. This will not only help you understand the research focus of the Buettner lab but also allow you to engage in meaningful discussions during the interview.
✨Tip Number 2
Network with current PhD students or researchers in the field of machine learning and oncology. They can provide valuable insights into the lab's culture and expectations, which can be beneficial for your application and interview preparation.
✨Tip Number 3
Prepare to discuss your programming skills, especially in R and Python. Be ready to share specific examples of projects where you've applied these languages, particularly in bioinformatics or machine learning contexts.
✨Tip Number 4
Highlight any experience you have with collaborative research, especially if it involves working with clinicians or experimental partners. This will demonstrate your ability to work in a multidisciplinary team, which is crucial for this position.
Diese Fähigkeiten machen dich zur top Bewerber*in für die Stelle: PhD Student for Machine Learning in Oncology
Tipps für deine Bewerbung 🫡
Understand the Research Focus: Familiarize yourself with the research group's focus on machine learning in oncology. Highlight your relevant experience and how it aligns with their mission to understand and modulate the tumor microenvironment.
Tailor Your CV: Customize your CV to emphasize your background in computer science, statistics, or bioinformatics. Include specific projects or experiences that demonstrate your knowledge of machine learning methods and programming skills in R or Python.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for cancer research and your interest in personalized oncology. Discuss your familiarity with probabilistic modeling and any relevant experience with bioinformatics algorithms.
Highlight Communication Skills: Since the role involves collaboration with researchers and clinicians, make sure to mention your English communication skills. Provide examples of how you've effectively communicated complex ideas in previous projects.
Wie du dich auf ein Vorstellungsgespräch bei DKTK partner site Frankfurt/Mainz - Machine Learning in Oncology vorbereitest
✨Show Your Passion for Oncology
Make sure to express your genuine interest in cancer research and how it aligns with your career goals. Discuss any relevant projects or experiences that highlight your commitment to this field.
✨Demonstrate Your Technical Skills
Be prepared to discuss your knowledge of machine learning methods, programming languages like Python or R, and any experience you have with bioinformatics algorithms. Providing examples of past projects can help illustrate your expertise.
✨Highlight Collaboration Experience
Since the role involves working closely with researchers and clinicians, emphasize your ability to collaborate effectively. Share examples of teamwork in previous projects, especially in interdisciplinary settings.
✨Prepare for Technical Questions
Anticipate questions related to probabilistic modeling and Bayesian methods. Brush up on these topics and be ready to explain complex concepts clearly, as this will demonstrate your depth of knowledge and communication skills.
