ICIQ FULLY-FUNDED PhD PROGRAMME 2026 – SECOND CALL
PhD 2026-06 LE
Supervisor: Luis Escobar
Project Title: Self-assembly of hybrid synthetic-biological systems for fundamental and biomedical applications
Number of positions: 1
Description of the project: The research group of Dr. Escobar is seeking a highly talented and motivated national or international candidate to pursue a PhD at the Institute of Chemical Research of Catalonia (ICIQ) in Tarragona, Spain. In partnership with the Universitat Rovira i Virgili (URV), ICIQ offers a PhD program in Chemical Science and Technology. The project focuses on the design and development of hybrid oligomers that combine synthetic and biological scaffolds, particularly host-guest systems with nucleic acids and peptides. The research will involve studying their fundamental properties and functions, as well as performing proof-of-principle studies to explore their potential biomedical applications.
Tasks to be performed by the PhD student:
Prepare synthetic host-guest systems.
Develop conjugation methodologies to combine host-guest systems with nucleic acids and peptides.
Investigate the fundamental properties and functions of hybrid oligomers.
Conduct proof-of-principle studies to explore potential biomedical applications.
Prepare progress reports and draft manuscripts for scientific publication.
Present research findings at group meetings and national/international conferences.
Participate in outreach activities.
Preferred skills or background:
Master’s degree in Chemistry or a closely related field.
Proficiency in English, both written and spoken.
Fundamental knowledge of routine characterization techniques.
Strong motivation to learn new techniques/methodologies and to develop soft skills.
Ability to work both independently and collaboratively.
Creative thinking and problem-solving skills.
Good time management and organizational skills.
Reliability and commitment to meeting deadlines.
Excellent academic record.
Ability to interact professionally with faculty, colleagues, and the general public.
This position will be funded by the Severo Ochoa Centers of Excellence Grant CEX2024-001469-S funded by MICIU/AEI/10.13039/501100011033.
PhD 2026-07 JS
Supervisors: Dr. Jesus Sanjosé-Orduna, Prof. Mónica H. Pérez-Temprano
Project Title: AI-Accelerated Photochemical Upgrading of Pollutants in Flow
Number of positions: 1
Description of the project:
The newly established research group of Dr. Sanjosé-Orduna is seeking a highly motivated candidate to pursue a doctoral degree in collaboration with the Pérez-Temprano group at the Institute of Chemical Research of Catalonia (ICIQ) and in partnership with the Universitat Rovira i Virgili (URV). The project focuses on the development of automated, data-driven workflows for the discovery and optimization of catalytic chemical transformations in continuous flow. The research will integrate robotic experimentation, microfluidic platforms and machine learning to systematically explore reaction space, generate high-quality datasets, and accelerate the optimization of synthetic methods for the recycling of harmful pollutants.
Tasks to be performed by the PhD student:
Design and execute experiments using automated platforms and microfluidic reactors.
Set up, operate, maintain and troubleshoot continuous-flow reactors.
Plan, collect, and curate high-quality, standardized experimental datasets for the training and validation of machine learning models.
Analyse experimental results and translate findings into actionable insights.
Prepare project reports and contribute in the draft of scientific publications.
Present research findings at group meetings and at national/international conferences.
Actively participate in the group's scientific life, engage with interdisciplinary collaborators, take part in outreach activities, and contribute to build a collaborative, inclusive and open research culture.
Preferred skills or background:
MSc degree (or equivalent) in Chemistry or a closely related discipline.
Excellent communication skills in English, written and spoken.
Ability to work both independently and as part of an interdisciplinary team.
Solid foundation in synthetic organic chemistry; prior exposure to catalysis, organometallic chemistry, photocatalysis or reaction development is an asset.
Interest in automation, robotics, or flow chemistry; hands-on experience is a plus but not required.
Willingness to learn data-driven approaches (machine learning, chemoinformatics, statistical modelling) and coding..
Enthusiasm for sustainable chemistry and a genuine interest in bridging academic research with industrial application.
PhD 2026-08 RM
Supervisor: Ruben Martin
Project Title: Catalytic Functionalization of sp3 C-H sites by Means of Chain-Walking
Number of positions: 1
Description of the project: The project aims at unravelling the potential of chain-walking reactions for enabling a series of carbon-carbon and carbon-heteroatom bond-forming reactions at distal, yet previously unfunctionalized, sp3 C-H reaction sites. The project will include the development of enantioselective transformations and enantioconvergent scenarios.
Tasks to be performed by the PhD student: The student will be responsible for tackling important challenges in the context of organometallic catalysis, optimization of the reaction conditions, isolation of putative reaction intermediates and kinetic experiments.
Preferred skills or background: The student should have proven expertise in (a) organometallic chemistry, (b) organic synthesis, (c) optimization of catalytic reactions and (d) proven profienciency in English.
PhD 2026-09 MG
Supervisor: Marcos García Suero
Project Title: Single-Atom Skeletal Editing to Impact Drug Discovery and Chemical Biology.
Number of positions: 1
Description of the project:
Skeletal editing processes that selectively delete, insert or exchange an atom in the skeleton of organic molecules have recently gained considerable momentum. Such processes and synthetic strategies are of significant interest to the pharmaceutical, agrochemical, perfumery and material industries due to their retrosynthetic simplicity and ability to diversify organic molecules at the skeletal level, giving access to a chemical space difficult or not possible to reach through conventional strategies. This project aims to develop a new concept in single-atom skeletal editing aligned with the chemistry developed in the group to impact drug discovery and chemical biology.
Tasks to be performed by the PhD student:
Capable of decision making, creative thinking and problem solving
Strong commitment for scientific research in a competitive environment
Capable of working independently and as a member/leader in a research team
Reliable in meeting commitments and deadlines
Demonstrated experience in writing research results in scientific journals
Preferred skills or background:
Completion of a Master’s degree in chemistry
Solid background in organic synthesis techniques
Familiarity with organometallic catalysis, photocatalysis, chemical biology
Proficiency in written and spoken English
Ability to take initiative and assume responsibility in the planning and development of a research project
PhD 2026-10 JB
Supervisor: José Augusto Berrocal
Project Title: Machine-learning-driven discovery of mechanophores
Number of positions: 1
Description of the project: Mechanical damage sensing is a key challenge in the development of next-generation polymeric materials for sustainability, energy technologies, and predictive maintenance. In this PhD project, the successful candidate will contribute to the AI-accelerated discovery of new mechanophores designed to report mechanical stress and damage in polymeric materials.
The project focuses on triarylmethane- and diarylmethane-based mechanophores, a class of force-responsive motifs pioneered by the Berrocal group, which undergo force-induced bond scission and generate strong optical signals. Possibly, the candidate will also create novel mechanophores different from those already established in the Berrocal group. By combining organic synthesis, flow chemistry and/or high‑throughput experimentation, and artificial intelligence, the PhD student will generate high-quality experimental datasets to train machine‑learning models aimed at discovering new, optimized mechanophores beyond current human intuition.
This PhD position is part of WP4 (AHEAD‑P) within the national AI project 2AID, a coordinated effort that integrates chemistry, materials science, robotics, and machine learning. While the PhD candidate will primarily perform experimental work, they will collaborate closely with AI researchers in the consortium and receive formal training in data science and machine learning, including courses and secondments within the network.
The PhD candidate will benefit from:
Access to a highly interdisciplinary consortium involving AI experts, roboticists, and chemists.
Training through dedicated AI schools, workshops, and secondments funded by the project.
State-of-the-art facilities for synthetic chemistry, polymer science, and automated experimentation.
A research topic at the forefront of AI-driven materials discovery with strong academic and industrial relevance.
Tasks to be performed by the PhD student:
The PhD student will be mainly responsible for experimental data generation and molecular synthesis, with the following tasks:
Design and synthesis of triarylmethane- and diarylmethane-based mechanophores using modern organic synthesis techniques.
Execution of batch synthesis and, where appropriate, continuous-flow or high-throughput experimentation (HTE) protocols to accelerate data generation.
Systematic tuning of mechanophore structure to modulate optical properties, force-activation thresholds, and chemical stability.
Collection of high‑quality experimental datasets (synthetic yields, optical properties, mechanical activation behavior) suitable for machine-learning training.
Generation and curation of chemical descriptors and experimental metadata to support AI-driven modeling efforts.
Integration of synthesized mechanophores into polymer matrices and preliminary evaluation of mechanoresponsive behavior.
Active interaction with consortium partners developing machine‑learning models, contributing experimental insight and feedback.
Participation in secondments and training activities within the project network to acquire competencies in machine learning, cheminformatics, and autonomous experimentation.
Dissemination of results through publications and conference presentations.
Preferred skills or background:
We are looking for a highly motivated experimental chemist with a strong interest in interdisciplinary research. Ideal candidates should have:
A Master’s degree in Chemistry, Organic Chemistry, Materials Science, or a closely related field.
Solid background in organic synthesis and standard laboratory techniques.
Strong motivation to work at the interface between chemistry, materials science, and artificial intelligence.
Interest in data-driven research and willingness to learn machine learning concepts (no prior ML experience required).
Experience with or interest in flow chemistry, automation, or high-throughput experimentation will be considered a strong asset, but is not mandatory.
Good programming skills (e.g., Python) are a plus but not required.
Ability to work independently and as part of a multidisciplinary, international team.
Good communication skills in English (B2 or higher).
PhD 2026-11 BP/EMF
Supervisor: Beatriz Prieto-Simón & Eugenia Martinez-Ferrero
Project Title: Multi-functional sensors for prompt diagnosis and therapy guidance
Number of positions: 1
Description of the project: Biosensors are analytical devices able to provide information about the pathogen causing an infection at its early stages. Specifically, electrochemical biosensors offer several advantages over culture-based methods, mainly based on their fast, highly reliable, sensitive and accurate quantitative response, cost effectiveness and possibility to be miniaturised. The project focuses on fabricating multi-layer mesoporous structures and testing their potential for the sequential detection of two pathogen-related targets. Such sequential detection is expected to provide unique specificity and sensitivity for the detection of a key pathogen, Staphylococcus epidermidis, which is a leading cause of catheter-related bloodstream infection.
Tasks to be performed by the PhD student:
Read and review relevant literature
Synthesis of hybrid materials by standard laboratory procedures
Site-specific functionalization of hybrid multi-layer materials
Characterization of the structure, surface chemistry and electrooptical properties of the material
Deposition of the prepared materials onto adequate supports
Analysis of the sensing performance of the materials
Analysis of the results and preparation of reports
Preferred skills or background:
Strong background in the synthesis of hybrid materials, their deposition and characterization techniques
Research experience in sensing
Demonstrated experience working as part of a small team and promoting a collaborative environment.
The candidate has to be highly motivated and used to work collaboratively.
Supervisor: Emilio Palomares
Project Title: Materials for Energy
Number of positions: 2
Description of the project: Understanding the relationship between structure of the materials and their performance provide valuable information for the smart design of functional materials that lead to efficient devices. The architecture of the solar cells consists of multistacked layers where each layer is made of a different material and plays a specific role. The project aims for the development of efficient hole transport materials (HTMs) and their subsequent application in perovskite solar cells. The analysis of the efficiency of the devices will be correlated with the molecular structure and optoelectronic properties of the HTMs. One position is focused on the design and synthesis of the HTMs and the second position in the fabrication and complete characterization of the devices.
Tasks to be performed by the PhD student:
Synthesis of organic, inorganic or hybrid materials by standard laboratory procedures
Characterization of the structure and electrooptical properties of the material
Deposition of the prepared materials onto adequate supports
Analysis of the performance of the materials in solar cells.
Analysis of the results and preparation of reports
Preferred skills or background:
Strong background in the synthesis of organic and inorganic materials and in the deposition and characterization techniques.
Research experience in a lab environment
Demonstrated experience working as part of a small team and promoting a collaborative environment.
The candidate has to be highly motivated and used to work collaboratively
PhD 2016-14 JLL
Supervisor: Julio Lloret-Fillol
Project Title: AI-driven robotics for catalyst development
Number of positions: 1
Description of the project:
The exploration and optimization of catalytic materials is critical in developing clean energy, environmental remediation, and chemical synthesis. Traditional experimental methods, still are labor-intensive, slow, and constrained by human limitations. By combining robots, artificial intelligence, and real-time data analysis, AI-driven robotic automation provides a game-changing approach that speeds up the synthesis, characterisation, and optimization of catalysts.
An autonomous robotic solution driven by AI is intended to be developed in this thesis for the high-throughput synthesis, quick characterisation, and data-driven optimization of catalytic materials. Through the use of machine learning (ML), real-time analytical methods, and closed-loop optimization with AI-agents, the platform will improve catalysis research's productivity, repeatability, and understanding.
Tasks to be performed by the PhD student:
Develop new catalyst for CO2 reduction reaction to fuels enabled by high-throughput synthesis and significantly speed up discovery.
Develop an AI-driven robotic system to automate catalyst research, improving improve efficiency, reproducibility, and scalability compared to manual methods.
Integrate realtime characterization techniques and closed-loop optimization together with machine learning for predicting properties and guiding experiments.
Develop the needed software and hardware, and the documentaction and scientific publications.
Preferred skills or background:
Experience or understanding of catalysis.
Familiarity with Robotic Automation and control systems, likely including experience with robotic arms and automated liquid handling systems.
Knowledge of AI and Machine Learning, particularly in the context of predictive modeling and optimization algorithms (e.g., neural networks, random forests, Bayesian optimization, reinforcement learning).
Willing to learn software and programming languages (Python), data acquisition and processing, machine learning (RDKit), optimization (Bayesian Optimization), experimental design (DoEpy), and computational chemistry software (Gaussian), to integrate hardware and software, including electronics and embedded electronics.
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