PRR Project

Summary

In recent years, there has been a growing demand for the development of methodologies capable of handling vast amounts of data, commonly referred to as "Big Data." This phenomenon is increasingly found across various fields, from telecommunications to environmental sciences, and is gaining increasing importance in health and commerce sectors. The inclination to preserve extensive datasets, particularly to fuel the widespread artificial intelligence algorithms prevalent on the internet, has become standard practice. However, managing and interpreting massive datasets present significant challenges. Extracting meaningful insights from data sets spanning hundreds of terabytes is a formidable task. Hence, there is a pressing need to devise more advanced methodologies for data reduction, pattern recognition, classification, and outlier detection. One domain of paramount societal significance where many of these challenges manifest is in health sciences. Medical and biological data tend to be intricate and is often afflicted by the well-known "curse of dimensionality."CEAUL considers this area as crucial for enhancement. Introducing new research methodologies is vital for various aspects of medical sciences, like cancer diagnosis or image reconstruction, aiding medical teams in early tumor detection, personalized medicine, and patient prognosis. By recruiting researchers to focus on developing novel methodologies, CEAUL aims to bolster its already significant presence in Biostatistics, albeit with a primary focus on applications. Furthermore, certain CEAUL members have established connections with prominent medical teams at the Champalimaud Foundation, particularly in studies involving Artificial Intelligence for breast cancer treatment, such as the 5M EUR EU-funded Cinderella Project, in which CEAUL members are the leading team for the statistical aspects of the clinical trial and the statistical analysis.In essence, this FCT-Tenure position is tailored for candidates keen on Big Data Analytics with Applications to Life Sciences and pioneering methodological advancements for more efficient problem-solving within the realms of Life Sciences and pharmaceutical domains, with the intent of publishing their findings in statistical journals. Establishing connections with the medical community to address pertinent real-world issues is anticipated to be readily feasible, given the substantial demand for researchers possessing high statistical proficiency. This collaborative endeavor is poised to yield compelling and publishable research outcomes in applied journals.The primary emphasis of the position is proficiency in statistical background, with a PhD in Statistics being a mandatory requirement. Additionally, the candidate should possess experience in teamwork and be available to engage in problems requiring theoretical developments.The candidate for the "Big Data Analytics with Applications to Life Sciences" FCT-Tenure is expected to achieve the following objectives: (1) Advance fundamental research, laying the groundwork for establishing a more theoretical subgroup within CEAUL. This subgroup aims to generate interest among scientifically well-prepared national and international students to pursue their PhD programs at Ciências ULisboa/CEAUL. Ideally, these students would receive supervision from integrated CEAUL members and co-supervision from members of foreign universities or research groups, thereby enhancing the visibility of CEAUL/Ciências ULisboa and its researchers. (2) Foster conditions conducive to submitting European Projects in collaboration with research units from foreign universities. (3) Initiate networks of researchers with a more theoretical focus, facilitating collaboration between medical teams addressing real-world problems and statisticians with a more application-driven approach.The CEAUL coordinator and executive commission acknowledge the need to fill the void left by the retirement of a significant group of senior members in recent years. Engaging highly skilled human resources is essential to elevate CEAUL to a standard of excellence and international recognition. While current CEAUL members excel in application-driven work, there is a need for individuals capable of developing fundamental statistical methodologies to strengthen the centre.