PRR Project

Summary

The contemporary problems to be addressed demand fitting complex models that entail heavy computational burden. While having access to very powerful computers and workstations, enhancing their performance requires concurrent advancements in efficient algorithms. Therefore, research in computational statistics is imperative to enhance both the quality and speed of producing the results. Spatial statistics underscores the importance of computational efficiency as it often plays a decisive role in yielding pertinent and meaningful solutions to various problems. As Tabler (1970) succinctly put it, "Everything is related to everything else, but near things are more related than distant things." Spatial (and spatio-temporal) models find widespread utility across domains such as Environment, Fisheries, Ecology, and Epidemiology. Bayesian statistics offers considerable flexibility compared to classical approaches, yet it entails significant computational intensity. Despite the traditionally time-consuming nature of Markov chain Monte Carlo (McMC) methods, ongoing research continues to propose algorithmic enhancements. In the interim, alternative avenues have emerged, with the integrated nested Laplace approximation (INLA), introduced by Rue, Martino, and Chopin (2009), standing out as a prominent and successful approach. The continuous refinement of the RINLA package, along with the accessibility of new models and statistical methods in an open-access format, underscores the dynamic nature of spatial and spatio-temporal modeling within a Bayesian framework. Notably, the computational demands of spatial (and spatio-temporal) models necessitate high-performance computing resources for efficient map production. CEAUL recognizes computational statistics, particularly within spatial modeling contexts, as a priority area for enrichment. The introduction of innovative research methodologies is crucial for ensuring alignment with leading international research groups.CEAUL has a longstanding and fruitful collaboration with the Portuguese Institute for Sea and Atmosphere (IPMA), with CEAUL members actively contributing to IPMA projects. Opportunities for additional collaborations exist with other research entities, such as the Dom Luiz Institute (IDL), particularly in the realm of wildfires, where joint internal projects are already underway. Moreover, spatial/spatio-temporal processes hold potential applications in public health, including mapping the spatial distribution and temporal trends of diseases, thereby offering avenues for collaboration with teams working in Health-related fields. In this context CEAUL also has a long tradition of collaborations with medical teams. These connections lead to the recent signature of a protocol between FCUL and the Faculty of Medicine, regulating and encouraging the growth of such collaborations. These groups, as well as the Life Sciences’ research groups collaborating with CEAUL can be the source of challenging projects in the spatio-temporal processes field.In essence, the FCT Tenure profile in Probability and Statistics seeks candidates interested in developing novel computational methodologies and efficient solutions within spatial/spatio-temporal contexts, with an emphasis on publishable contributions to statistical journals. Collaborations with entities like IPMA and IDL will provide real-world problem scenarios, facilitating the generation of compelling and publishable applications in applied journals.The key requirement for candidates aspiring to the position is proficiency in statistical background, necessitating a PhD in Statistics. Additionally, a demonstrated aptitude for teamwork and a willingness to engage in theoretical developments are essential attributes.Candidates for this position are expected to achieve the following objectives: (1) Advance fundamental research, laying the groundwork for establishing a methodologically-driven subgroup within CEAUL. This subgroup aims to attract scientifically adept national and international students to pursue their PhD programs at Ciências ULisboa/CEAUL. Ideally, these students would be supervised by integrated CEAUL members and co-supervised by members of other universities or research groups, thereby enhancing the visibility of CEAUL/ Ciências ULisboa and its researchers. (2) Foster conditions conducive to submitting joint European Projects with foreign university research units. (3) Establish a network of methodologically-driven researchers.The CEAUL coordinator, along with the executive commission, recognizes the imperative to fill the void left by retiring senior members, necessitating the engagement of highly skilled human resources to elevate CEAUL to a standard of excellence and international recognition. While the current CEAUL members excel in applications, there is a pressing need for individuals capable of developing fundamental statistical methodologies to fortify the center´s capabilities.???????