The main objectives of the internship will be: 1. to develop a graphical interface to train vocalization annotation models, to visualize their performance and to re-annotate parts of the dataset accordingly (in a similar fashion as semi-supervised learning); 2. to develop the corresponding software backend: data management (audio and annotations), serving and local persistence of the models (MLOps); 3. to collaborate with the project members to define the needs, establish the specifications or integrate pre-existing tools. This objective also implies collaborating with international researchers, and making an open source tool available to the public. The development will be incremental: a first prototype will allow to train models and to present their evaluation on the interface. A second prototype will offer advanced editing possibilities of the dataset (re-annotation of parts of the audio according to the results of the model), and the final version will integrate advanced analysis tools (dataset errors detection, spectrograms dimensionality reduction for visualization and/or clustering, syntactic analysis of song sequences, ...)

The University of Wisconsin-Madison's Information School seeks highly qualified candidates for up to two tenured positions in information sciences. These faculty positions will be academic nine-month, tenure-track appointments at the Associate Professor level, to start August 2024. Applications at the Professor level may be considered in exceptional cases. Applications are specifically encouraged in, but not limited to, the following areas: Natural language processing and information retrieval, e.g., applied natural language processing, text analysis, text and multimedia retrieval, recommendation systems, conversational systems. Computational social sciences, e.g., analytics and modeling of political behavior; computational analysis of social networks; algorithms and social media analytics; social simulation of organizational behavior. Policy analysis or policy-making studies of information or data security/risk/assurance, privacy, data governance, or data management. ML/AI, computation, and the future of work. Computational and information technologies in relation to children and/or elderly populations.

The ReproPsy & e-ReproNim Fellowship Programmes are opportunities for early career researchers (ECRs) from EU and UK institutions to join a community dedicated to advancing open and robust data practices in Psychological and Neuroscientific research. Fellows will receive financial support to fund training to enhance skills in software and data management, participate in online events, contribute to projects such as scoping and designing training needs, writing training material, and more.

The National Institute on Drug Abuse (NIDA) is seeking a GS-15 Data Scientist for the Office of the Director. The incumbent will serve as a Data Scientist and Senior Advisor to the NIDA Director and other leadership positions, on matters related to health informatics and data science, data management, emerging technologies, opportunities for collaboration, data standards and policy within the scope of the NIDA mission. Responsibilities include planning and carrying out quality control programs, selecting statistical methods for quality control analysis, ensuring the reliability and consistency of data, providing technical expertise and project leadership to advance strategic initiatives in data- and knowledge-driven methods, and developing collaborations with researchers and administrators in neuroscience.

One of the most important questions in psychology and neuroscience is understanding how the outside world maps to internal representations. Classical psychophysics approaches to this problem have a number of limitations: they mostly study low dimensional perpetual spaces, and are constrained in the number and diversity of participants and experiments. As ecologically valid perception is rich, high dimensional, contextual, and culturally dependent, these impediments severely bias our understanding of perceptual representations. Recent technological advances—the emergence of so-called “Virtual Labs”— can significantly contribute toward overcoming these barriers. Here I present a number of specific strategies that my group has developed in order to probe representations across a number of dimensions. 1) Massive online experiments can increase significantly the amount of participants and experiments that can be carried out in a single study, while also significantly diversifying the participant pool. We have developed a platform, PsyNet, that enables “experiments as code,” whereby the orchestration of computer servers, recruiting, compensation of participants, and data management is fully automated and every experiment can be fully replicated with one command line. I will demonstrate how PsyNet allows us to recruit thousands of participants for each study with a large number of control experimental conditions, significantly increasing our understanding of auditory perception. 2) Virtual lab methods also enable us to run experiments that are nearly impossible in a traditional lab setting. I will demonstrate our development of adaptive sampling, a set of behavioural methods that combine machine learning sampling techniques (Monte Carlo Markov Chains) with human interactions and allow us to create high-dimensional maps of perceptual representations with unprecedented resolution. 3) Finally, I will demonstrate how the aforementioned methods can be applied to the study of perceptual priors in both audition and vision, with a focus on our work in cross-cultural research, which studies how perceptual priors are influenced by experience and culture in diverse samples of participants from around the world.