High-performance scientific applications are becoming increasingly complex, in particular because of the coupling of parallel codes. This results in applications having a complex structure, characterized by multiple deploy-time parameters, such as the number of processes of each code. In order to optimize the performance of these applications, the parameters have to be carefully chosen, a process which is highly resource dependent. However, the abstractions provided by current Resource Management Systems (RMS) - either submitting rigid jobs or enumerating a list of moldable configurations - are insufficient to efficiently select resources for such applications. This paper introduces CooRM, an RMS architecture that delegates resource selection to applications while still keeping control over the resources. The proposed architecture is evaluated using a simulator which is then validated with a proof-of-concept implementation on Grid'5000. Results show that such a system is feasible and performs well with respect to scalability and fairness.