Adding ATLAS Tile proposal, IFIC org and logo

_gsocorgs/2026/ific.md

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+---
+title: "IFIC"
+author: "Luca Fiorini"
+layout: default
+organization: IFIC
+logo: IFIC-logo.png
+description: |
+  The [IFIC](https://ific.uv.es), Corpuscular Physics Institute, is a joint centre between the Spanish Research Council (Consejo Superior de Investigaciones Científicas, CSIC) and the University of Valencia devoted to research in Nuclear, Particle and Astroparticle Physics and its applications to Medical Physics and to other fields of Science and Technology. physicists and engineers are probing the fundamental structure of the universe. They use the world's largest and most complex scientific instruments to study the basic constituents of matter – the fundamental particles.
+---
+
+{% include gsoc_proposal.ext %}

_gsocproposals/2026/proposal_ATLAS_TILESIGNAL.md

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+---
+title: AI-Accelerated Reconstruction for the ATLAS Tile Calorimeter at the HL-LHC
+layout: gsoc_proposal
+project: ATLAS
+year: 2026
+organization:
+  - CERN
+  - IFIC
+difficulty: medium
+duration: 350
+mentor_avail: June-October
+project_mentors:
+  - email: Luca.Fiorini@cern.ch
+    first_name: Luca
+    last_name: Fiorini
+    organization: IFIC
+    is_preferred_contact: yes
+  - email: fernando.carrio@cern.ch
+    first_name: Fernando 
+    last_name: Carriò
+    organization: IFIC, CERN
+    is_preferred_contact: yes
+---
+
+## Description
+
+ATLAS will produce data at an unprecedented scale at the High-Luminosity LHC (HL-LHC). This project offers the opportunity to work on a real problem at the intersection of machine learning, real-time computing, and the experimental physics frontier, with direct relevance for the future ATLAS detector upgrade.
+
+The student will develop and evaluate deep-learning-based signal reconstruction methods for the ATLAS Tile Calorimeter (TileCal), comparing them with classical algorithms and exploring how to deploy efficient inference on modern hardware accelerators (GPU and/or FPGA-friendly models).
+
+Recent studies indicate that AI-based reconstruction can be implemented on FPGAs and outperform classical methods in amplitude and timing estimation, especially in challenging pile-up regimes. However, for real deployment, models must satisfy strict constraints on:
+- latency (sub-microsecond scale, trigger-compatible),
+- resource usage (memory and compute),
+- robustness and reproducibility.
+
+This project contributes to ongoing R&D for the TileCal readout upgrade and the development of sustainable, energy-efficient computing strategies and it addresses a real challenge for the next decade of LHC physics. The developed methods are also highly transferable to other domains requiring fast reconstruction, such as medical imaging and industrial monitoring.
+
+## Task Ideas
+
+- Understand the TileCal signal reconstruction problem
+- Implement baseline reconstruction methods
+- Develop a compact ML model for reconstruction
+- Benchmark and compare performance
+- Explore model optimization for deployment
+
+## Expected Results and Milestones
+
+- A reproducible reconstruction pipeline (baseline + ML)
+- Quantitative performance comparison under HL-LHC-like conditions
+- A trained compact model suitable for fast inference
+- A final technical report and presentation
+
+## Requirements
+
+- Python programming skills
+- ML fundamentals 
+- Time series analysis or anomaly detection experience
+- Interest in scientific software optimization 
+
+## AI Policy
+
+AI assistance is permitted. The applicant is fully accountable for all code and results and must disclose AI use for non-routine work (e.g., algorithm design, architecture, complex reasoning). Routine use for grammar or formatting does not need to be reported.
+
+## How to Apply
+
+Email mentors with a brief background and interest in Computing/particle physics. Please include "gsoc26" in the subject line. Mentors will provide an evaluation task after submission.
+
+## Resources
+
+- [ATLAS Experiment](https://atlas.cern/)
+- [ATHENA Framework](https://atlas-software.docs.cern.ch/athena/basics/intro/)
+- [TileCal IFIC Group](https://tilecal.ific.uv.es/tilecalweb/)