Summary
- Profile Type
- Research & Development Request
- POD Reference
- RDRES20250618024
- Term of Validity
- 19 June 2025 - 19 June 2026
- Company's Country
- Spain
- Type of partnership
- Research and development cooperation agreement
- Targeted Countries
- All countries
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General information
- Short Summary
- A leading Spanish private health institution is seeking a postdoctoral researcher to conduct research in embryology and reproductive medicine as part of an international team within the framework of MSCA. Candidates must hold a PhD in a quantitative or biomedical field and possess strong skills in Python, AI, and data science. Experience with real-world clinical data, deep learning (e.g., TensorFlow or PyTorch), and reproductive medicine, particularly time-lapse embryo imaging, is highly valued.
- Full Description
-
A world-class Spanish private healthcare institution is seeking a motivated and qualified postdoctoral researcher to join a research team in Biomarkers and Genomic Reproductive Medicine via MSCA - Marie Skłodowska-Curie Actions. This position is ideal for candidates with a strong background in computational biology, bioinformatics, or related fields, interested in applying advanced data science to reproductive health.
Responsibilities:
Conduct advanced research in embryology and reproductive medicine.
Contribute to the publication of scientific results.
Collaborate with an international team and participate in outreach activities.
Candidate Profile:
Applicants should hold a PhD in Computational Biology, Bioinformatics, Biomedical Engineering, Biostatistics, Computer Science or a related quantitative field with a clear biomedical focus. Alternatively, candidates with a PhD in Biomedicine or Biotechnology are welcome, provided they demonstrate strong skills in programming, data science, and machine learning.
Key competencies:
A solid foundation in computational methods and biological systems.
Proficiency in Python and data science libraries (e.g. Pandas, NumPy, Scikit-learn).
Experience with deep learning (TensorFlow or PyTorch), ideally applied to morphokinetic video data.
Data management skills, including SQL, data cleaning, integration, and feature engineering.
A strong record of peer-reviewed publications in computational or biomedical applications.
Hands-on experience with real-world clinical data is essential.
Background in reproductive medicine, particularly in ART, embryology, and time-lapse imaging, is highly valued.
The Research Group:
It is embedded in the field of Clinical Embryology, with a strong focus on the integration of Artificial Intelligence (AI) to improve the evaluation, classification, and diagnosis of gametes (sperm and oocytes) and embryos. It’s a multidisciplinary team working at the intersection of reproductive biology, embryology, and data science, aiming to enhance the precision and objectivity of reproductive assessment tools.
One of the main research line is the development of AI-based models to analyze both static images and time-lapse videos of embryonic development, with the aim of identifying phenotypic and developmental characteristics that can serve as non-invasive biomarkers for embryo quality and implantation potential. A key objective is to refine embryo selection by integrating morphological and kinetic features, ultimately improving clinical outcomes in assisted reproduction.
They also explore non-invasive methods for assessing the chromosomal status of embryos, using indirect markers derived from image analysis and culture media, aspiring to complement or eventually replace traditional invasive techniques for preimplantation genetic testing.
In parallel, the group conducts research on sperm quality and selection methods. They are particularly focused on developing AI-assisted techniques to assess sperm morphology, motility, and functional competence in a reliable and reproducible manner. These tools aim to optimize sperm selection for fertilization and improve outcomes in assisted reproductive technologies (ART).
Furthermore, they investigate the impact of cryopreservation and thawing processes on both oocytes and embryos. Our studies examine how these procedures affect embryo viability and reproductive success, seeking to identify predictive indicators and refine current laboratory protocols.
Through these interconnected research lines, they significantly contribute to the advancement of personalized and evidence-based reproductive medicine, leveraging the power of AI to bring precision and standardization to the embryology laboratory.
They offer a dynamic, supportive, and multidisciplinary work environment, opportunities for professional development and continuous training, access to state-of-the-art research infrastructure and tools, and a two-year full-time contract. - Advantages and Innovations
-
Infertility affects millions worldwide, and embryo selection is crucial for IVF success. Current methods rely on morphology and invasive genetic testing (PGT-A), which can be costly, risky, and inaccurate due to mosaicism. The project develops a non-invasive preimplantation genetic test (niPGT-A) using a new genetic method that eliminates the need for biopsy. Artificial Intelligence will assess embryo development patterns, and the use of omics will help in the assessment of implantation success.
It will set the foundation for future commercialization, transforming reproductive medicine and making IVF more accessible and effective for all.
This non-invasive embryo selection technology has the potential to significantly impact society by improving assisted reproduction outcomes, reducing costs, and enhancing accessibility to IVF treatments. Infertility affects millions worldwide, and current selection methods rely on invasive and expensive techniques, limiting their availability to many patients. This innovation will reduce physical and emotional stress for patients undergoing IVF.
A more advanced selection process increases implantation success rates, minimizing the number of cycles required to achieve pregnancy. It leads to reduced financial burdens on families, making fertility treatments more accessible to a broader population.
Additionally, this innovative approach promotes ethical advancements in reproductive medicine by preventing unnecessary embryo discarding due to false-positive genetic diagnoses. This aligns with social and ethical principles by maximizing the potential of each embryo while maintaining patient autonomy.
This innovative technology will also contribute to reducing the disparities in access to advanced fertility treatments by offering a scalable and cost-effective solution adaptable to different healthcare systems.
Beyond reproductive medicine, this innovation paves the way for broader applications in early-stage diagnostics. - Technical Specification or Expertise Sought
-
The ideal candidate for this postdoctoral grant would possess a PhD from a highly quantitative field such as Computational Biology, Bioinformatics, Biomedical Engineering, Biostatistics, or Computer Science with a clear focus on biomedical applications.
Alternatively, candidates with a PhD in Biomedicine or Biotechnology would be strongly considered, provided they can demonstrate a significant and robust portfolio of work in programming, data science, and the application of machine learning.
The key is a strong foundational background that blends computational expertise with an understanding of biological systems, ensuring they have the theoretical knowledge to tackle complex clinical data challenges.
The perfect candidate must have high-level proficiency in Python and its core data science libraries like Pandas, NumPy, and Scikit-learn. Given the nature of the research, direct experience with deep learning frameworks such as TensorFlow or PyTorch is highly desirable for analyzing complex datasets like morphokinetic videos. This should be complemented by practical skills in data management, including proficiency in SQL for querying Electronic Medical Record (EMR) databases and expertise in the critical steps of data cleaning, integration, and feature engineering, which are essential for building reliable predictive models.
The candidate should have a solid track record of peer-reviewed publications that showcase the application of AI or computational methods to solve clinical or biological problems. Direct experience working with messy, real-world clinical data is paramount. The most competitive applicants will have prior exposure to the field of reproductive medicine, including a foundational knowledge of assisted reproductive technologies (ART), embryology, and specifically, the analysis of morphokinetic data from time-lapse embryo imaging. - Sustainable Development Goals
- Goal 8: Decent Work and Economic Growth
- Goal 3: Good Health and Well-being
- Goal 15: Life on Land
- Goal 4: Quality Education
- Goal 10: Reduced Inequality
- Goal 17: Partnerships to achieve the Goal
- Goal 9: Industry, Innovation and Infrastructure
- Goal 12: Responsible Consumption and Production
Partner Sought
- Expected Role of a Partner
-
Marie Slodowska-Curie PF (Postdoctoral Fellowships) 2025 supports researchers’ careers and fosters excellence in research. The Postdoctoral Fellowships action targets researchers holding a PhD who wish to carry out their research activities abroad, acquire new skills, and develop their careers. PFs help researchers gain experience in other countries, disciplines, and non-academic sectors.
European Postdoctoral Fellowships are open to researchers moving within Europe or coming to Europe from another part of the world to pursue their research career. These fellowships take place in an EU Member State or Horizon Europe Associated Country and can last between 1 and 2 years. Researchers of any nationality can apply provided they have not worked for more than 12 months in the last 3 years in Spain.
Interested researchers submit an application together with a host organisation, which is a world-class private health institution based in Valencia - Spain, deeplly specilised in Reproductive Medicine. area.
Researchers interested in this MSCA PFs opportunity should have a PhD degree at the time of the deadline for applications.
Applicants who have successfully defended their doctoral thesis but who have not yet formally been awarded the doctoral degree will also be considered eligible to apply.
The researcher must have a maximum of eight years experience in research, from the date of the award of their PhD degree, years of experience outside research and career breaks will not count towards the above maximum, nor will years of experience in research in third countries, for nationals or long-term residents of EU Member States or Horizon Europe Associated Countries who wish to reintegrate to Europe.
The candidate should comply with mobility rules: they must not have resided or carried out their main activity (work, studies, etc.) in the country of the beneficiary (for European Postdoctoral Fellowships). - Type and Size of Partner
- SME 50 - 249
- SME <=10
- Big company
- University
- SME 11-49
- R&D Institution
- Other
- Type of partnership
- Research and development cooperation agreement
Call details
- Framework program
- Excellent science
- Call title and identifier
-
MSCA Postdoctoral Fellowships 2025 (HORIZON-MSCA-2025-PF)
- Anticipated project budget
-
200.000-230.000 euros
- Coordinator required
-
No
- Deadline for EoI
- Deadline of the call
- Project duration in weeks
-
104
- Web link to the call
- https://bit.ly/3G7KCQ9
- Project title and acronym
-
NextGenPower: AI and other approaches to foster the success of reproductive medicine.
- Excellent science
Dissemination
- Technology keywords
- 06001005 - Diagnostics, Diagnosis
- 06001009 - Gene - DNA Therapy
- 06001012 - Medical Research
- 06001013 - Medical Technology / Biomedical Engineering
- 06003001 - Bioinformatics
- Market keywords
- 05007007 - Other medical/health related (not elsewhere classified)
- 05001002 - In-vitro diagnostics
- 05001007 - Other diagnostic
- 05005018 - Medical Physics, Physiology
- 05005021 - Medical computer sciences
- Targeted countries
- All countries