Optimization

…connects systems theory, statistics, optimization and formal methods, examining recurrent networks, attention mechanisms and state-space models. Work includes theoretical analysis, experimental algorithms and scientific publication. The advertised gross monthly salary is €2,300. The advert lists an October 2026 target start alongside a November application deadline; applicants should confirm start-date flexibility.

…including simulation, inference, optimization, randomized numerical methods or scalable high-dimensional algorithms. The role combines independent funded research with teaching and service. A relevant PhD and research and teaching excellence are required. Salary: CAD 140,000–190,000 annually. Submit a cover letter, CV, research statement, teaching dossier and three reference letters through AcademicJobsOnline by December 7, 2026. Appli

The Division of Engineering and Applied Science at the California Institute of Technology invites applications for tenure-track and tenured faculty positions. Preference will be given to candidates at the Assistant Professor level; however, extraordinary applicants at the Associate Professor or Full Professor level may also be considered. We seek highly qualified candidates who are committed to a career in teaching, mentoring, and research excellence in the broad areas of Information, Systems, and Computing. Applicants should have a record of innovation in a relevant research domain,

…scientific computing, probability, optimization and theory with substantial computational content. The role combines an active research programme with undergraduate and graduate teaching and interdisciplinary collaboration. The advertised salary range is USD 90,000–135,000. Submit a cover letter, CV, research and teaching statements, a generative-AI-use statement and four references, including one on teaching. Applications received by

…equations, scientific computing, optimization, nonlinear dynamics, mathematical physics, network science and multiscale simulation. Faculty teach, supervise students, seek research funding and contribute to curriculum development. English is the working language. Applications received before 1 November 2026 receive full consideration; the search continues until filled.

…The work uses Modelica, Python, optimization and AI, with laboratory or field validation and industrial collaboration. This two-year postdoctoral appointment has monthly pay of USD 8,266–8,921. Priority review is December 30, 2026. Hybrid staff must live within 150 miles of Berkeley Lab.

Three talks examine machine learning within optimization. J. Carlos Castro partitions distributed networks into connected regions sharing an interpretable predictor, contrasting selection of existing models with training on pooled data. A variable-neighbourhood search enforces connectivity; dynamic programming provides exact solutions for particular spatial configurations and a comparison for general networks. Ilaria Ciocci studies nonlinear support-vector machines with hard feature cardinality limits. An exact convex mixed-integer quadratic reformulation uses auxiliary variables for polynomi

…sets combine continuous trajectory optimization and discrete task sequencing in one problem. A layered graph constructed from an exact convex partition of free space selects an optimal task order and trajectory through a shortest-path calculation, exact up to finite Bézier parameterization. Its structure corresponds to Bellman–Held–Karp dynamic programming for the travelling-salesperson problem, retaining singly exponen

…task and thus, different degrees of optimization. Looking at the entire recording session, optimality does not always imply aiming for the greatest values, but rather, as suggested by heuristics

…even after hundreds of millions of optimization steps. The loss of plastici

The ‘quantification’ of perception is arguably both one of the most important and most difficult aspects of perception study. This is particularly true in visual perception, in which the evaluation of the perceptual threshold is a pillar of the experimental process. The choice of the correct adaptive psychometric procedure, as well as the selection of the proper parameters, is a difficult but key aspect of the experimental protocol. For instance, Bayesian methods such as QUEST, require the a priori choice of a family of functions (e.g. Gaussian), which is rarely known before the experiment, as

…which provide a solution to an optimization problem that can be implemented at the single-neuron level using only local inf

Gradient-based optimization powered by the backpropagation algorithm proved to be the pivotal method in the training of non-spiking artificial neural networks. At the same time, spiking neural networks hold the promise for efficient processing of real-world sensory data by communicating using discrete events in continuous time. We derive the backpropagation algorithm for a recurrent network of spiking (leaky integrate-and-fire) neurons with hard thresholds and show that the backward dynamics amount to an event-based backpropagation of errors through time. Our derivation uses the jump condition

…this hypothesis, we adopted an optimization approach. Leveraging recent ad