Job Description

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity. Learn more at https://www.jnj.com

Job Function:

Career Programs

Job Sub

Function:

Post Doc - Data Analytics & Computational Sciences

Job Category:

Career Program

All Job Posting Locations:

Allschwil, Basel-Country, Switzerland, Beerse, Antwerp, Belgium, High Wycombe, Buckinghamshire, United Kingdom

:

Postdoctoral Researcher - LLM for Trial Operations


We are searching for the best talent for a Postdoctoral Researcher position in Data Analytics & Computational Sciences focused on demonstrating advanced technologies, including Large Language Models (LLMs), Generative Pre-trained Transformers (GPT), and Retrieval-Augmented Generation (RAG), to enhance clinical trial protocol design. This is a 2 year fixed term contract and can be based in Belgium (Beerse), UK (High Wycombe) and Switzerland (Basel).


Remote working options will be considered on a case by case basis.

Position Overview:

As a Postdoctoral Researcher, you will contribute to innovative projects aimed at improving the design and execution of clinical trials through the application of ground breaking data analytics and machine learning techniques. You will have opportunities for individual development tailored to your professional goals while continuing to build your expertise in the field of computational sciences.

Key Responsibilities:

Apply LLMs and GPT technologies to streamline and enhance the extraction of key features from clinical trial protocols, including disease, drug, eligibility criteria, and endpoints. Apply RAG techniques to improve the retrieval of relevant information from existing clinical trial data, facilitating better-informed protocol design. Develop predictive models using sophisticated machine learning techniques to assess trial feasibility, timeline estimates, and operational efficiency based on protocol content. Demonstrated experience in data processing and engineering, with the ability to design, build, and maintain efficient data pipelines that facilitate effective data utilization, as well as the creation, curation, and maintenance of key datasets. Collaborate with multidisciplinary teams to integrate data-driven insights into the protocol design process, ensuring that the complexities of clinical trials are adequately addressed. Design and implement innovative tools and frameworks for protocol enhancement, incorporating feedback from end-users to iteratively improve processes and outcomes.

Qualifications / Requirements:

Ph.D. in Data Analytics, Computational Sciences, Biomedical Informatics, or a related field. Strong background in machine learning, natural language processing, and data modeling. Experience with large language models (LLMs), GPT, and RAG technologies and their applications, healthcare or clinical research preferred but not required.. Experience in statistical modeling and analysis, including methods such as regression, time series analysis, or Bayesian modeling. Excellent analytical, problem-solving, and communication skills. Ability to work collaboratively in a multidisciplinary team environment.

Programming Requirements:

Proficiency in programming languages such as Python and R, with experience in data manipulation and analysis libraries (e.g., Pandas, NumPy, scikit-learn). Familiarity with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch , Hugging Face Transformers). Experience with database management and querying languages (e.g., SQL). Understanding of version control systems (e.g., Git) for collaborative coding.

Desired Skills:

Familiarity with

clinical trial design, phases, and regulatory requirements

and

clinical trial

operational metrics. * U nderstanding of statistical analysis in clinical trials, including concepts such as hypothesis testing, p-values, confidence intervals, and common statistical tests used in trial data analysis.